Add letter to all elements but not last

Description

This function allows to add 'addChr' to all entries, without the last entry

Usage

.addLetterWoLast(x, addChr)

Arguments

Value

This function returns a modified character vector

See Also

paste; used in cutAtMultSites

Examples

.addLetterWoLast(c("abc","efgh"),"Z")

Calculate ratios for each column to each column of reference-matrix

Description

This function calculates ratio(s) for each column of matrix 'x' versus all/each column(s) of matrix 'y' (reference)

Usage

.allRatioMatr1to2(x, y, asLog2 = TRUE, sumMeth = "mean", callFrom = NULL)

Arguments

Value

This function returns a numeric vector or matrix in dimension of 'x' (so far summarize all ratios from mult division from mult ref cols as mean or median )

See Also

makeMAList, grep

Examples

.allRatioMatr1to2(matrix(11:14, ncol=2), matrix(21:24, ncol=2))

Search character-string and cut either before or after

Description

This function extracts/cuts text-fragments out of txt following specific anchors defined by arguments cutFrom and cutTo.

Usage

.allRatios(dat, ty = "log2", colNaSep = "_")

Arguments

Value

This function returns a numeric vector

See Also

makeMAList, grep

Examples

.allRatios(matrix(11:14, ncol=2))

Summarize along columns of multiple arrays in list

Description

This function allows summarizing along columns of multiple arrays in list

Usage

.arrLstMean(
  arrLst,
  sumType = "mean",
  arrOutp = FALSE,
  signifDig = 3,
  formatCheck = FALSE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

array (1st dim will be summary along cols, rows will be layers of 3rd array-dim

See Also

used in cutArrayInCluLike

Examples

.datSlope(c(3:6))

Summarize along columns of mult arrays in list

Description

This function allows summarizing along columns of mult arrays in list

Usage

.arrLstSEM(
  arrLst,
  arrOutp = FALSE,
  signifDig = 3,
  formatCheck = FALSE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

array (1st dim will be summary along cols, rows will be layers of 3rd array-dim ie dim(arrLst[[1]])[3])

See Also

used in cutArrayInCluLike

Examples

.datSlope(c(3:6))

Convert anything to data.frame

Description

This function allows converting anything to data.frame

Usage

.asDF2(z)

Arguments

Value

data.frame

See Also

as.data.frame

Examples

.asDF2(c(3:6))

Get series of values after last discontinuity

Description

This function aims to get series of values after last discontinuity

Usage

.breakInSer(x, getFrom = "last")

Arguments

Value

This function returns a numeric vector of reduced length

See Also

dist

Examples

.breakInSer(c(11:14,16:18))

Bring most extreme to center

Description

This function aims to bring most extreme value to center

Usage

.bringToCtr(aa, ctr, ctrFa = 0.75)

Arguments

Value

This function returns an adjusted numeric vector

See Also

dist

Examples

.bringToCtr(11:14, 9)

Check argument names

Description

This function allows checking of argument names

Usage

.checkArgNa(x, argNa, lazyEval = TRUE)

Arguments

Value

This function returns a elongated character vector

See Also

chartr

Examples

.checkArgNa("Abc",c("ab","Ab","BCD"))

Check list of arrays for consistent dimensions of all arrays

Description

This function allows to check list of arrays for consistent dimensions of all arrays

Usage

.checkConsistentArrList(
  arrLst,
  arrNDim = 3,
  fxName = NULL,
  varName = NULL,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

list

See Also

used in cutArrayInCluLike

Examples

.datSlope(c(3:6))

Convert to simple vector (similar to unlist)

Description

This function allows converting 'dat' (may be list, data.frame etc) to simple vector, more elaborate than unlist()

Usage

.checkConvt2Vect(dat, toNumeric = TRUE)

Arguments

Value

character (or numeric) vector

See Also

unlist; used in equLenNumber

Examples

aa <- matrix(11:14, ncol=2)
.checkConvt2Vect(aa)

Check Factor

Description

This function was designed to check a factor object

Usage

.checkFactor(
  fac,
  facNa = NULL,
  minLev = 2,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a corrceted/adjusted factor

See Also

factor

Examples

.checkFactor(gl(3,2))

checkFileNameExtensions Function for checking file-names.

Description

checkFileNameExtensions Function for checking file-names.

Usage

.checkFileNameExtensions(fileNa, ext)

Arguments

Value

modified character vector

Examples

.checkFileNameExtensions("testFile.txt","txt")

Check argument for Location of legend

Description

This function allows checking an argument for Location of legend, if value provided not found as valid, it returns 'defLoc

Usage

.checkLegendLoc(
  legLoc,
  defLoc = "topright",
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a character vector designing the potential location of legend

See Also

legend

Examples

.checkLegendLoc("abc")

Compare 'dat' to confindence interval of linare model 'lMod' (eg from lm())

Description

This function allows to compare 'dat' to confindence interval of linare model 'lMod' (eg from lm())

Usage

.checkLmConfInt(dat, lMod, level = 0.95)

Arguments

Value

This function returns a logical vector for each value in 2nd col of 'dat' if INSIDE confid interval

See Also

searchLinesAtGivenSlope

Examples

set.seed(2016); dat1 <- matrix(c(runif(200)+rep(1:10,20)),ncol=10)

Check regression arguments

Description

This function allows to check arguments for linear regression. Used as argument checking for regrBy1or2point and regrMultBy1or2point

Usage

.checkRegrArguments(inData, refList, regreTo, callFrom = NULL)

Arguments

Value

list

See Also

append; lrbind

Examples

.datSlope(c(3:6))

Automatic choice of colors

Description

This function allows to do automatic choice of colors: if single-> grey, if few -> RColorBrewer, if many : gradient green -> grey/red

Usage

.chooseGrpCol(
  nGrp,
  paired = FALSE,
  alph = 0.2,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a character vector with color codes

See Also

rgb; colorAccording2

Examples

.chooseGrpCol(4)	

Combine annotation information from list of matrixes

Description

This function allows to combine information (annotation) from list of matrixes (ie replace when NA), using always the columns specified in 'useCol' (numeric)

Usage

.combineListAnnot(
  lst,
  useCol = 1:2,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a single matrix of combined (non-redundant) info

See Also

used in cutArrayInCluLike

Examples

.datSlope(c(3:6))

Compare by distance/difference

Description

This function allows to compare by distance/difference

Usage

.compareByDiff(dat, limit, distVal = FALSE)

Arguments

Value

This function returns a list with close matches of 'x' to given 'y', the numeric value dependes on 'sortMatch' (if FASLE then always value of 'y' otherwise of longest of x&y)

See Also

findCloseMatch, checkSimValueInSer, and also .compareByLogRatio, for convient output countCloseToLimits

Examples

cc <- list(aa=11:14, bb=c(13.1,11.5,14.3,20:21))

Compare by log-ratio

Description

This function allows to compare by log-ratio

Usage

.compareByLogRatio(dat, limit, distVal = FALSE)

Arguments

Value

This function returns a list with close matches of 'x' to given 'y', the numeric value dependes on 'sortMatch' (if FASLE then always value of 'y' otherwise of longest of x&y)

See Also

findCloseMatch, checkSimValueInSer, and also .compareByDiff, for convient output countCloseToLimits

Examples

cc <- list(aa=11:14, bb=c(13.1,11.5,14.3,20:21))
.compareByLogRatio(cc, 1) 

Compare by PPM

Description

This function allows to compare by ppm

Usage

.compareByPPM(dat, limit, distVal = FALSE)

Arguments

Value

This function returns a list with close matches of 'x' to given 'y', the numeric value dependes on 'sortMatch' (if FASLE then always value of 'y' otherwise of longest of x&y)

See Also

findCloseMatch, checkSimValueInSer, and also .compareByDiff, for convient output countCloseToLimits

Examples

cc <- list(aa=11:14, bb=c(13.1,11.5,14.3,20:21))
.compareByPPM(cc, 1) 

Search (complementing) columns for best coverage of non-NA data for rowNormalization (main)

Description

This function was designed to complete the selection of columns of sparse matrix 'dat' with sets of 'nCombin' columns at complete 'coverage' Context : In sparse matrix 'dat' search subsets of columns with some rows as complete (no NA).

Usage

.complCols(x, dat, nCombin)

Arguments

Value

This function returns a matrix of column-indexes complementing (nCombin rows)

See Also

rowNormalize

Examples

.complCols(3, dat=matrix(c(NA,12:17,NA,19),ncol=3), nCombin=3)

Compose sequence of (function-)calls

Description

This function was designed for tracing the hierarchy of function-calls. It allows to remove any tailing space or ': ' from 'callFrom' (character vector) and return with added 'newNa' (+ 'add2Tail')

Usage

.composeCallName(newNa, add2Head = "", add2Tail = " : ", callFrom = NULL)

Arguments

Value

character vector (history of who called whom)

See Also

paste

Examples

.composeCallName("newFunction", callFrom="initFunction")

Convert numeric matrix to numeric

Description

Take matrix and return vector

Usage

.convertMatrToNum(matr, useCol = NULL)

Arguments

Value

numeric vector

See Also

matrix

Examples

.convertMatrToNum(matrix(1:6, ncol=2))

Convert/standardize names of 'query' to standard names from 'ref'

Description

This function converts/standardizes names of 'query' to standard names from 'ref' (list of possible names (char vect) where names define standardized name). It takes 'query' as character vector and return character vecor (same length as 'query') with 'converted/corrected' names

Usage

.convertNa(query, ref, partMatch = TRUE)

Arguments

Value

This function returns a character vector

Examples

daPa <- matrix(c(1:5,8,2:6,9), ncol=2)

Avoid duplicating items between 'curNa' and 'newNa' by incrementing digits after 'extPref' (in newNa)

Description

This function aims to avoid duplicating items between 'curNa' and 'newNa' by incrementing digits after 'extPref' (in newNa)

Usage

.corDuplItemsByIncrem(newNa, curNa, extPref = "_s")

Arguments

Value

This function returns the corrected input vector newNa

See Also

duplicated

Examples

.corDuplItemsByIncrem(letters[1:6], letters[8:4])

Search character-string and cut either before or after

Description

This function extracts/cuts text-fragments out of txt following specific anchors defined by arguments cutFrom and cutTo.

Usage

.cutAtSearch(
  x,
  searchChar,
  after = TRUE,
  silent = TRUE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a modified character vector

See Also

grep

Examples

.cutAtSearch("abcdefg","de")

Cut string to get all variants from given start with min and max length

Description

This function allows truncating character vector to all variants from given start, with min and optonal max length Used to evaluate argument calls without giving full length of argument

Usage

.cutStr(txt, startFr = 1, minLe = 1, maxLe = NULL, reverse = TRUE)

Arguments

Value

This function returns a character vector

See Also

used in pasteC; substr

Examples

.cutStr("abcdefg", minLe=2)

Model linear regression and optional plot

Description

This function allows to model a linear regression and optionally to plot the results

Usage

.datSlope(
  dat,
  typeOfPlot = "sort",
  toNinX = FALSE,
  plotData = FALSE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

numeric vector with intercept and slope, optional plot

See Also

append; lrbind

Examples

.datSlope(c(3:6))

Return File-name Extensions Including Double Extensions (eg txt.gz)

Description

This function allows retreiving extensions to filenames similar to file_ext, but also alllows to retreive selected double-extensions (txt.gz). The leading dor will ne excluded similar to file_ext

Usage

.doubleExt(fiNa, termExt = c("gz", "zip"))

Arguments

Details

The argument termExt allows specifying additional (terminal) extensions to be recognized.

Value

This function returns a character vector with file-extensions (excluding the leading dot). (Only purely alphanumeric extensions are recognized.)

See Also

file_ext, checkFilePath

Examples

fi <- c("ab","ab.c","ab.c.gz","ab.d","ab.d.zip","ab.e")
.doubleExt(fi)

Extract NA-neighbour values

Description

This function allows extracting NA-neighbour value

Usage

.extrNAneighb(x, grp)

Arguments

Value

snumeric vector

See Also

unique, nonAmbiguousNum, faster than firstOfRepeated which gives more detail in output (lines/elements/indexes of omitted)

Examples

.extrNAneighb(c(11:14,NA), rep(1,5))

Extract number(s) before capital character

Description

This function aims to extract number(s) before capital character

Usage

.extrNumHeadingCap(x)

Arguments

Value

This function returns a numeric vector

See Also

grep, nchar

Examples

.extrNumHeadingCap(" 1B ")

Extract numbers before separator followed by alphabetic character

Description

This function aims to extract number(s) before separator followed by alphabetic character (return named numeric vector, NAs when no numeric part found)

Usage

.extrNumHeadingSepChar(x, sep = "_")

Arguments

Value

This function returns a numeric vector

See Also

nchar

Examples

.extrNumHeadingSepChar(" 1B ")

Filter for size

Description

This function aims to filter for size

Usage

.filtSize(x, minSize = 5, maxSize = 36)

Arguments

Value

list of filtered input

See Also

filtSizeUniq; correctToUnique, unique, duplicated

Examples

aa <- 1:10

Filter nodes & edges for extracting networks (main) This function allows extracting and filtering network-data based on fixed threshold (limInt) and add sandwich-nodes (nodes inter-connecting initial nodes) out of node-based queries.

Description

Filter nodes & edges for extracting networks (main)

This function allows extracting and filtering network-data based on fixed threshold (limInt) and add sandwich-nodes (nodes inter-connecting initial nodes) out of node-based queries.

Usage

.filterNetw(
  lst,
  remOrphans = TRUE,
  reverseCheck = TRUE,
  filtCol = 2,
  callFrom = NULL,
  silent = FALSE,
  debug = FALSE
)

Arguments

Value

This function returns a matrix or data.frame

See Also

filterNetw and other CRAN package dedeicated to networks

Examples

ab <- 1:10  

Filter 3-dim array of numeric data (main)

Description

Filtering of matrix or array x (may be 3-dim array) according to fiTy and checkVa

Usage

.filterSw(x, fiTy, checkVa, indexRet = TRUE)

Arguments

Value

This function returns either index (position within 'x') or concrete (filtered) result

See Also

filt3dimArr; filterList; filterLiColDeList;

Examples

arr1 <- array(11:34, dim=c(4,3,2), dimnames=list(c(LETTERS[1:4]),
  paste("col",1:3,sep=""),c("ch1","ch2")))
filt3dimArr(arr1,displCrit=c("col1","col2"),filtCrit="col2",filtVal=7)
.filterSw(arr1, fiTy="inf", checkVa=7)

Find overlap instances among range of values in lines

Description

This function aims to find overlap instances among range of values in lines of 'x' (typically give just min & max)

Usage

.findBorderOverlaps(x, rmRedund = FALSE, callFrom = NULL)

Arguments

Value

This function returns a matrix with line for each overlap found, cols 'refLi' (line no), 'targLi' (line no), 'targCol' (col no)

See Also

nchar

Examples

aa <- 11:15

Get first minimum

Description

This function allows to find the first minimum of a numeric vector

Usage

.firstMin(x, positionOnly = FALSE)

Arguments

Value

numeric vector

See Also

which.min

Examples

.firstMin(c(4,3:6))

fuse 2 instances of 3dim arr as mult cols in 3dim array

Description

This function allows fusing 2 instances of 3dim arr as mult cols in 3dim array (ie fuse along 2nd dim, increase cols)

Usage

.fuse2ArrBy2ndDim(arr1, arr2, silent = FALSE, debug = FALSE, callFrom = NULL)

Arguments

Value

This functuin returns a numeric vector with numer of non-numeric characters (ie not '.' or 0-9))

See Also

array

Examples

aa <- 11:15

Get A value for each group of replicates

Description

This function calculates the 'A' value (ie group mean) for each group of replicates (eg for MA-plot)

Usage

.getAmean(dat, grp)

Arguments

Value

This function returns a numeric vector

See Also

makeMAList

Examples

.getAmean(matrix(11:18, ncol=4), gl(2,2))

Get A value for each group of replicates based on comp

Description

This function calculates the 'A' value (ie group mean) for each group of replicates (eg for MA-plot) comp is matrix telling which groups to use/compare, assuming that dat are already group-means)

Usage

.getAmean2(dat, comp)

Arguments

Value

This function returns a numeric vector

See Also

makeMAList

Examples

.getAmean(matrix(11:18, ncol=4), gl(2,2))

Get M value for each group of replicates based on comp

Description

This function calculates the 'M' value (ie log-ratio) for each group of replicates based on comp (eg for MA-plot) comp is matrix telling which groups to use/compare, assuming that dat are already group-means)

Usage

.getMvalue2(dat, comp)

Arguments

Value

This function returns a numeric vector

See Also

makeMAList

Examples

.getAmean(matrix(11:18, ncol=4), gl(2,2))

Grow tree

Description

This function allows growing tree-like structures (data.tree objects)

Usage

.growTree(tm, setX, addToObj = NULL)

Arguments

Value

list

See Also

buildTree

Examples

.datSlope(c(3:6))

Segment (1-dim vector) 'dat' into clusters

Description

This function allows aegmenting (1-dim vector) 'dat' into clusters. If 'automClu=TRUE ..' first try automatic clustering, if too few clusters, run km with length(dat)^0.3 clusters This function requires the package NbClust to be installed.

Usage

.insp1dimByClustering(
  dat,
  automClu = TRUE,
  cluChar = TRUE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns clustering (class index) or (if 'cluChar'=TRUE) list with clustering and cluster-characteristics

See Also

searchLinesAtGivenSlope

Examples

set.seed(2016); dat1 <- matrix(c(runif(200)+rep(1:10,20)),ncol=10)

Inspect 'matr' and check if 1st line can be used/converted as header

Description

This function inspects 'matr' and check if 1st line can be used/converted as header. If colnames of 'matr' are either NULL or 'V1',etc the 1st row will be tested if it contains any of the elements (if not, 1st line won't be used as new colnames) If 'numericCheck'=TRUE, all columns will be tested if they can be converted to numeric

Usage

.inspectHeader(
  matr,
  headNames = c("Plate", "Well", "StainA"),
  numericCheck = TRUE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a matrix vector or data.frame similar to input

See Also

head for looking at first few lines

Examples

ma1 <- matrix(letters[1:6], ncol=3, dimnames=list(NULL,c("ab","Plate","Well")))
.inspectHeader(ma1) 

Refine/filter 'dat1' (1dim dataset, eg cluster) with aim of keeping center of data

Description

This function allows to refine/filter 'dat1' (1dim dataset, eg cluster) with aim of keeping center of data. It is done based on most freq class of histogramm keep/filter data if 'core' (

Usage

.keepCenter1d(
  dat1,
  core = NULL,
  keepOnly = TRUE,
  displPlot = FALSE,
  silent = TRUE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns the index of values retained or if 'keepOnly' return list with 'keep' index and 'drop' index

See Also

searchLinesAtGivenSlope

Examples

set.seed(2016); dat1 <- matrix(c(runif(200)+rep(1:10,20)),ncol=10)

Remove all columns where all data are not finite

Description

This function aims to remove all columns where all data are not finite

Usage

.keepFiniteCol(
  dat,
  msgStart = NULL,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a corrected matrix or data.frame

See Also

renameColumns; is.finite

Examples

ma1 <- matrix(c(1:5, Inf), ncol=2)
.keepFiniteCol(ma1)

Check if vector may be numeric content

Description

This function allows to checking if a given vector may be numeric content

Usage

.mayBeNum(x, pattern = NULL)

Arguments

Value

This functions returns a logical/boolean vector for each of the elements of 'x'

See Also

numeric; convMatr2df

Examples

.mayBeNum(c(3:6))

Rescale respective to specific group

Description

This function allows to rescale data 'x' so that specific group 'grpNum' gets normalized to predefined value 'grpVal'. In normal case x will be multiplied by 'grpVal' and devided by value obtained from 'grpNum'. If summary of 'grpNum-positions' or 'grpVal' is 0, then grpVal will be attained by subtraction of summary & adding grpVal

Usage

.medianSpecGrp(x, grpNum, grpVal, sumMeth = "median", callFrom = NULL)

Arguments

Value

numeric vector

See Also

which.min

Examples

.firstMin(c(4,3:6))

Merge Multiple Matrices (main)

Description

This function allows merging of multiple matrix-like objects from an initial list.

Usage

.mergeMatrices(
  inpL,
  mode = "intersect",
  useColumn = 1,
  extrRowNames = FALSE,
  na.rm = TRUE,
  argL = NULL,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a matrix containing all selected columns of the input matrices to fuse

See Also

mergeMatrixList, merge, mergeMatrices for separate entries

Examples

mat1 <- matrix(11:18, ncol=2, dimnames=list(letters[3:6],LETTERS[1:2]))

find closest neighbour to numeric vector

Description

This function aims to find closest neighbour to numeric vector

Usage

.minDif(z, initOrder = TRUE, rat = TRUE)

Arguments

Value

This function returns a matrix with index,value,dif,best

See Also

dist

Examples

.minDif(c(11:15,17))

Distances beteenw sorted points of 2-columns

Description

This function returns distances beteenw sorted points of 2-column matrix 'x'

Usage

.neigbDis(x, asSum = TRUE)

Arguments

Value

This function returns a numeric vector with distances

Examples

daPa <- matrix(c(1:5,8,2:6,9), ncol=2)
.neigbDis(daPa)

Normalize columns of 2dim matrix to common linear regression fit

Description

This function aims to normalize columns of 2dim matrix to common linear regression fit within range of 'useQuant'

Usage

.normConstSlope(
  mat,
  useQuant = c(0.2, 0.8),
  refLines = NULL,
  diagPlot = TRUE,
  plotLog = "",
  datName = NULL,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a numeric vector

See Also

normalizeThis

Examples

aa <- matrix(1:12, ncol=3)

Main Normalization function

Description

This function aims to normalize a matrix or data.frame by columns. It assumes all checks have been done before calling this function.

Usage

.normalize(
  dat,
  meth,
  mode,
  param,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a numeric vector

See Also

normalizeThis

Examples

aa <- matrix(1:12, ncol=3)
.normalize(aa,"median",mode="proportional",param=NULL)

Return position of 'di' (numeric vector) which is most excentric (distant to 0), starts with NAs as most excentric

Description

This function aims to return position of 'di' (numeric vector) which is most excentric (distant to 0), starts with NAs as most excentric It is used for identifying/removing (potential) outliers. Note : this fx doesn't consider reference distrubutions, even with "perfect data" 'nMost' points will ba tagged !

Usage

.offCenter(di, nMost = 1)

Arguments

Value

This function returns a integer/numeric vector (indicating index)

See Also

use in presenceFilt; diff

Examples

.offCenter(11:14)

Paste-concatenate all columns of matrix

Description

This function allows paste columns

Usage

.pasteCols(mat, sep = "")

Arguments

Value

simplified/non-redundant vector/matrix (ie fewer lines for matrix), or respective index

See Also

unique, nonAmbiguousNum, faster than firstOfRepeated which gives more detail in output (lines/elements/indexes of omitted)

Examples

.pasteCols(matrix(11:16,ncol=2), sep="_")

Pie plot for counting results

Description

This function allows to inspect results of table or uniqCountReport on a pie-plot Note : fairly slow for long vectors !!

Usage

.plotCountPie(
  count,
  tit = NULL,
  col = NULL,
  radius = 0.9,
  sizeTo = NULL,
  clockwise = FALSE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

vector with counts of n (total), nUnique (wo any repeated), nHasRepeated (first of repeated), nRedundant), optional figure

See Also

uniqCountReport, correctToUnique, unique

Examples

.plotCountPie(table(c(1:5,4:2)))

Add lower caps to character vector

Description

This function allows adding all content as lower caps to/of character vector

Usage

.plusLowerCaps(x)

Arguments

Value

This function returns a elongated character vector

See Also

chartr

Examples

.plusLowerCaps(c("Abc","BCD"))

Calculate residues of (2-dim) linear model 'lMod'-prediction of/for 'dat'

Description

This function calculates residues of (2-dim) linear model 'lMod'-prediction of/for 'dat' (using 2nd col of 'useCol' ) (indexing in 'dat', matrix or data.frame with min 2 cols), using 1st col of 'useCol' as 'x'. It may be used for comparing/identifying data close to regression (eg re-finding data on autoregression line in FT-ICR)

Usage

.predRes(dat, lMod, regTy = "lin", useCol = 1:2)

Arguments

Value

This function returns a numeric vector of residues (for each line of dat)

See Also

searchLinesAtGivenSlope

Examples

set.seed(2016); dat1 <- matrix(c(runif(200)+rep(1:10,20)),ncol=10)

Raise all values close to lowest value

Description

This function aims to raise all values close to lowest value to end up as at value of 'raiseTo'. This is done independently for each col of mat. This function sets all data to common raiseTo (which is min among all cols)

Usage

.raiseColLowest(
  mat,
  raiseTo = NULL,
  minFa = 0.1,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a numeric vector with numer of non-numeric characters (ie not '.' or 0-9))

See Also

nchar

Examples

aa <- 11:15

Remove columns indicated by col-number

Description

This function aims to remove columns indicated by col-number

Usage

.removeCol(matr, rmCol)

Arguments

Value

This function returns an matrix or data.frame

See Also

dist

Examples

aa <- matrix(1:6, ncol=3)
.removeCol(aa, 2)

Search for (empty) columns conaining only entries defined in 'searchFields' and remove such columns

Description

This function aims to search for (empty) columns conaining only entries defined in 'searchFields' and remove such columns. If 'fromBackOnly' =TRUE .. only tailing empty columns will be removed (other columns with "empty" entries in middle will be kept). If ”=TRUE columns containing all NAs will be excluded as well This function will also remove columns containing (exculsively) mixtures of the various 'searchFields'.

Usage

.removeEmptyCol(
  dat,
  fromBackOnly = TRUE,
  searchFields = c("", " ", "NA.", NA),
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a corrected matrix or data.frame

See Also

renameColumns; is.finite

Examples

ma1 <- matrix(c(1:5, NA), ncol=2)
.removeEmptyCol(ma1)

Replace Special Characters

Description

This function allows replacing special characters Note that (most) special characters must be presented with protection for grep and sub.

Usage

.replSpecChar(x, findSp = c("\\(", "\\)", "\\$"), replBy = "_")

Arguments

Value

This function returns a corrceted/adjusted factor

See Also

factor

Examples

.replSpecChar(c("jhjh(ab)","abc"))

Trim character string: keep only text before 'sep'

Description

Trim character string: keep only text before 'sep' (length=1 !)

Usage

.retain1stPart(chr, sep = " = ", offSet = 1)

Arguments

Value

This function returns a modified character vector

See Also

substr

Examples

.retain1stPart("abc = def")

row group CV (main)

Description

This function calculates CVs for matrix with multiple groups of data, ie one CV for each group of data.

Usage

.rowGrpCV(x, grp, means)

Arguments

Value

This function returns a matrix of CV values

See Also

rowGrpCV, rowCVs, arrayCV, replPlateCV

Examples

set.seed(2016); dat1 <- matrix(c(runif(200)+rep(1:10,20)),ncol=10)
grp1 <- gl(4,3,labels=LETTERS[1:4])[2:11]
head(.rowGrpCV(dat1, grp1,  .rowGrpMeans(dat1, grp1)))

row group mean (main)

Description

This function calculates CVs for matrix with multiple groups of data, ie one CV for each group of data.

Usage

.rowGrpMeans(x, grp, na.replVa = NULL, na.rm = TRUE)

Arguments

Value

This function returns a matrix of mean values per row and group of replicates

See Also

rowGrpCV, rowCVs, arrayCV, replPlateCV

Examples

set.seed(2016); dat1 <- matrix(c(runif(200)+rep(1:10,20)),ncol=10)
grp1 <- gl(4,3,labels=LETTERS[1:4])[2:11]
head(.rowGrpMeans(dat1, grp1))

row group sd (main)

Description

This function calculates sd for matrix with multiple groups of data, ie one sd for each group of data.

Usage

.rowGrpSds(x, grp)

Arguments

Value

This function returns a matrix of sd values per row and group of replicates

See Also

rowGrpCV, rowCVs, arrayCV, replPlateCV

Examples

set.seed(2016); dat1 <- matrix(c(runif(200)+rep(1:10,20)),ncol=10)
grp1 <- gl(4,3,labels=LETTERS[1:4])[2:11]
head(.rowGrpSds(dat1, grp1))

row group rowSums per group (main)

Description

This function calculates row-sums for matrix with multiple groups of data, with multiple groups of data, ie one sd for each group of data.

Usage

.rowGrpSums(x, grp, na.replVa = NULL, na.rm = TRUE)

Arguments

Value

This function returns a matrix of row-sums for matrix with multiple groups of data

See Also

rowGrpCV, rowCVs, arrayCV, replPlateCV

Examples

set.seed(2016); dat1 <- matrix(c(runif(200)+rep(1:10,20)),ncol=10)
grp1 <- gl(4,3,labels=LETTERS[1:4])[2:11]
head(.rowGrpSums(dat1, grp1)) 

Row-normalization procedure on matrix or data.frame 'dat'

Description

This function was performs a row-normalization procedure on matrix or data.frame 'dat'

Usage

.rowNorm(
  dat,
  refLi,
  method,
  proportMode,
  maxFact = 10,
  fact0val = 10,
  retFact = FALSE,
  callFrom = NULL,
  debug = FALSE,
  silent = FALSE
)

Arguments

Value

This function returns a matrix of normalized data same dimensions as 'dat'

See Also

rowNormalize

Examples

.rowNorm(matrix(11:31, ncol=3), refLi=1, method="mean", proportMode=TRUE)

Obtain normalization factor (main)

Description

This function was designed to obtain normalization factors.

Usage

.rowNormFact(
  dat,
  combOfN,
  comUse,
  method = "median",
  refLi = NULL,
  refGrp = NULL,
  proportMode = TRUE,
  minQuant = NULL,
  maxFact = 10,
  omitNonAlignable = FALSE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a matrix of column-indexes complementing (nCombin rows)

See Also

rowNormalize

Examples

ma1 <- matrix(11:41, ncol=3)

Scale between 0 and 1 (main)

Description

This function rescales between 0 and 1

Usage

.scale01(x)

Arguments

Value

This function returns a numeric vector of same length with re-scaled values

See Also

scaleXY , scale

Examples

.scale01(11:15)

Rescale respective to specific group

Description

This function allows to rescale data 'x' so that 2 specific groups get normalized to predefined values (and all other values follow proportionally) 'grp1Num' and 'grp2Num' should be either numeric for positions in 'x' or character for names of 'x'; if 'grp1Num' and/or 'grp2Num' design mulitple locations: perform median or mean summarization, according to 'sumMeth'

Usage

.scaleSpecGrp(
  x,
  grp1Num,
  grp1Val,
  grp2Num = NULL,
  grp2Val = NULL,
  sumMeth = "mean",
  callFrom = NULL
)

Arguments

Value

numeric vector

See Also

which.min

Examples

.firstMin(c(4,3:6))

Scale between min and max value (main)

Description

This function rescales between user-defined min and max values

Usage

.scaleXY(x, minim = 2, maxim = 3)

Arguments

Value

This function returns a matrix of CV values

See Also

scaleXY , scale

Examples

.scaleXY(11:15, min=1, max=100)

Cut string to get all variants from given start with min length, depreciated

Description

This function is depreciated, please use /cutStr instead ! This function allows truncating character vector to all variants from given start, with min and optonal max length Used to evaluate argument calls without giving full length of argument

Usage

.seqCutStr(txt, startFr = 1, minLe = 1, reverse = TRUE)

Arguments

Value

This function returns a character vector

See Also

pasteC; substr

Examples

.seqCutStr("abcdefg", minLe=2)

Set lowest value to given value

Description

This function aims to set lowest value of x to value 'setTo'

Usage

.setLowestTo(x, setTo)

Arguments

Value

This function returns a numeric vector

See Also

nchar

Examples

.setLowestTo(9:4, 6)

Choose most frequent or middle of sorted vector

Description

This function chooses the (first) most frequent or middle of sorted vector, similar to the concept of mode

Usage

.sortMid(x, retVal = TRUE)

Arguments

Value

This function returns a numeric verctor

See Also

simple/partial functionality in summarizeCols, checkSimValueInSer

Examples

.sortMid(11:14)
.sortMid(rep("b",3))

Reorganize array by reducing dimension 'byDim' (similar to stack() for data-frames)

Description

This function aims to reorganize an array by reducing dimension 'byDim' (similar to stack() for data-frames) It returns an array/matrix of 1 dimension less than 'arr', 1st dim has more lines (names as paste with '_')

Usage

.stackArray(arr, byDim = 3)

Arguments

Value

This function returns an array/matrix of 1 dimension less than 'arr', 1st dim has more lines (names as paste with '_')

See Also

dist

Examples

(arr1 <- array(11:37, dim=c(3,3,3)))
.stackArray(arr1, 3)

Summarize columns of matrix (or data.frame) 'x' using apply (main)

Description

This function summarizes columns of matrix (or data.frame) 'x' using apply In case of character entries the 'median' of sorted values will be returned

Usage

.summarizeCols(
  x,
  me = "median",
  nEq = FALSE,
  vectAs1row = TRUE,
  supl = NULL,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

vector with summary for each column (unless 'me=="summary"', in this case a matrix or list will be returned )

See Also

summarizeCols, table table

Examples

m1 <- matrix(c(28,27,11,12,11,12), nrow=2, dimnames=list(1:2,c("y","x","ref")))
.summarizeCols(m1, me="median")

Trim from end (Deprecated)

Description

Deprecated Version - This function allows trimming/removing redundant text-fragments from end

Usage

.trimFromEnd(x, ..., callFrom = NULL, debug = FALSE, silent = TRUE)

Arguments

Value

This function returns a modified character vector

See Also

trimRedundText; Inverse : Find/keep common text keepCommonText; you may also look for related functions in package stringr

Examples

txt1 <- c("abcd_ccc","bcd_ccc","cde_ccc")
.trimRight(txt1)

Trim from start (Deprecated)

Description

Deprecated Version - This function allows trimming/removing redundant text-fragments from start

Usage

.trimFromStart(
  x,
  ...,
  minNchar = 1,
  silent = TRUE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a modified character vector

See Also

trimRedundText; Inverse : Find/keep common text keepCommonText; you may also look for related functions in package stringr

Examples

txt1 <- c("abcd_ccc","bcd_ccc","cde_ccc")
.trimLeft(txt1)  # replacement

Trim From Left Side

Description

This function allows trimming/removing redundant text-fragments from left side.

Usage

.trimLeft(x, minNchar = 1, silent = TRUE, debug = FALSE, callFrom = NULL)

Arguments

Value

This function returns a modified character vector

See Also

trimRedundText; Inverse : Find/keep common text keepCommonText; you may also look for related functions in package stringr

Examples

txt1 <- c("abcd_ccc","bcd_ccc","cde_ccc")
.trimLeft(txt1)

Trim From Right Side

Description

This function allows trimming/removing redundant text-fragments from right side.

Usage

.trimRight(x, minNchar = 1, silent = TRUE, debug = FALSE, callFrom = NULL)

Arguments

Value

This function returns a modified character vector

See Also

trimRedundText; Inverse : Find/keep common text keepCommonText; you may also look for related functions in package stringr

Examples

txt1 <- c("abcd_ccc","bcd_ccc","cde_ccc")
.trimRight(txt1)

Check regression arguments

Description

This function is an enhanced version of unique, names of elements are maintained

Usage

.uniqueWName(
  x,
  splitSameName = TRUE,
  silent = TRUE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

vector like input

See Also

unique

Examples

aa <- c(a=11, b=12,a=11,d=14, c=11)
.uniqueWName(aa)
.uniqueWName(aa[-1]) # value repeated but different name

Convert numeric vector to matrix

Description

Take (numeric) vector and return matrix, if 'colNa' given will be used as colname

Usage

.vector2Matr(x, colNa = NULL, rowsKeep = TRUE)

Arguments

Value

matrix

See Also

matrix

Examples

.vector2Matr(c(3:6))

Express difference as ppm

Description

This function transforms offset (pariwise-difference) between 'x' & 'y' to ppm (as normalized difference ppm, parts per million, ie (x-y)/y ). This type of expressiong differences is used eg in mass-spectrometry.

Usage

XYToDiffPpm(x, y, nSign = NULL, silent = FALSE, debug = FALSE, callFrom = NULL)

Arguments

Value

This function returns a numeric vector of (ratio-) ppm values

See Also

ratioToPpm for classical ppm

Examples

set.seed(2017); aa <- runif(10,50,900)
cbind(x=aa,y=aa+1e-3,ppm=XYToDiffPpm(aa,aa+1e-3,nSign=4)) 

Add text before file-extension

Description

This function helps changing charater srings like file-names and allows adding the character vector 'add' (length 1) before the extension (defined by last '.') of the input string 'x'. Used for easily creating variants/additional filenames but keeping current extension.

Usage

addBeforFileExtension(
  x,
  add,
  sep = "_",
  silent = FALSE,
  callFrom = NULL,
  debug = FALSE
)

Arguments

Value

modified character vector

Examples

addBeforFileExtension(c("abd.txt","ghg.ijij.txt","kjh"),"new")

Linear rescaling of numeric vertor or matrix

Description

adjBy2ptReg takes data within window defined by 'lims' and determines linear transformation so that these points get the regression characteristics 'regrTo', all other points (ie beyond the limits) will follow the same transformation. In other words, this function performs 'linear rescaling', by adjusting (normalizing) the vector 'dat' by linear regression so that points falling in 'lims' (list with upper & lower boundaries) will end up as 'regrTo'.

Usage

adjBy2ptReg(
  dat,
  lims,
  regrTo = c(0.1, 0.9),
  refLines = NULL,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a matrix (of same dimensions as inlut matrix) with normalized values

See Also

normalizeThis

Examples

set.seed(2016); dat1 <- round(runif(50,0,100),1)
## extreme values will be further away :
adjBy2ptReg(dat1,lims=list(c(5,9), c(60,90)))
plot(dat1, adjBy2ptReg(dat1, lims=list(c(5,9),c(60,90))))

Adjust Value With Different Decimal Prefixes To Single Prefix Plus Unit

Description

This function provides help converting values with with different unit-prefixes to a single prefix-unit type. This can be used to convert a vector of mixed prefixes like 'p' and 'n'. Any text to the right of the unit will be ignored.

Usage

adjustUnitPrefix(
  x,
  pref = c("z", "a", "f", "p", "n", "u", "m", "", "k", "M", "G"),
  unit = "sec",
  sep = c("_", ".", " ", ""),
  minTrimNChar = 0,
  returnType = c("NAifInvalid", "allText"),
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Details

The aim of this function if to allow adjusting a vector containing '100pMol' and '1nMol' to '100pMol' and '1000pMol' for better downstream analysis. Please note that the current version recognizes and converts only interger values; decimals or scientific writing won't be recognized properly. The resultant numeric vector expresses all values as lowest prefix unit level. In case of invalid entries NAs will be returned.

Please note that decimal/comma digits will not be recognized properly, since the function will consider (by default) the decimal sign as just another separator.

To avoid special characters (which may not work on all operating-systems) the letter 'u' is used for 'micro'.

Value

This function returns a character vector (same length as input) with adjusted unified decimal prefix and adjusted numeric content, the numeric content only is also giben in the names of the output

See Also

convToNum; checkUnitPrefix; trimRedundText

Examples

adjustUnitPrefix(c("2.psec abc","20 fsec etc"), unit="sec")

x1 <- c("50_amol", "5_fmol","250_amol","100_amol", NA, "500_amol", "500_amol", "1_fmol")
adjustUnitPrefix(x1, unit="mol")                   

x2 <- c("abCc 500_nmol ABC", "abEe5_umol", "", "abFF_100_nmol_G", "abGg 2_mol", "abH.1 mmol")
rbind( adjustUnitPrefix(x2, unit="mol", returnType="allText") , 
  adjustUnitPrefix(x2, unit="mol", returnType="trim"),
  adjustUnitPrefix(x2, unit="mol", returnType=""))

Append vectors or lists, without duplcating common elements

Description

This function allows combining two vectors or lists without duplicating common content (definded by name of list-elements).

Usage

appendNR(x, y, rmDuplicate = TRUE, silent = FALSE, callFrom = NULL)

Arguments

Details

When setting the argument rmDuplicate=FALSE the function will behave like append.

Value

If both x and y are vectors, the output will be a vector, otherwise it will be a list

See Also

append; lrbind

Examples

li1 <- list(a=1, b=2, c=3)
li2 <- list(A=11, B=12, c=3)
appendNR(li1, li2)
append(li1, li2) 

CV of array

Description

arrayCV gets CVs for replicates in 2 or 3 dim array and returns CVs as matrix. This function may be used to calculate CVs from replicate microtiter plates (eg 8x12) where replicates are typically done as multiple plates, ie initial matrixes that are the organized into arrays.

Usage

arrayCV(arr, byDim = 3, silent = TRUE, callFrom = NULL)

Arguments

Value

matrix of CV values

See Also

rowCVs, rowGrpCV, replPlateCV

Examples

set.seed(2016); dat1 <- matrix(c(runif(200) +rep(1:10,20)), ncol=10)
head(arrayCV(dat1,byDim=2))

Organize Data as Separate List-Entries

Description

asSepList allows reorganizing most types of input into a list with separate numeric vectors. For example, matrixes or data.frames will be split into separate columns (differnt to partUnlist which maintains the original structure). This function also works with lists of lists. This function may be helpful for reorganizing data for plots.

Usage

asSepList(
  y,
  minLen = 4,
  asNumeric = TRUE,
  exclElem = NULL,
  sep = "_",
  fillNames = TRUE,
  silent = FALSE,
  callFrom = NULL,
  debug = FALSE
)

Arguments

Value

This function returns a list, partially unlisted to vectors

See Also

partUnlist, unlist

Examples

bb <- list(fa=gl(2,2), c=31:33, L2=matrix(21:28,nc=2),
  li=list(li1=11:14, li2=data.frame(41:44)))
asSepList(bb)
## multi data-frame examples
ca <- data.frame(a=11:15, b=21:25, c=31:35)
cb <- data.frame(a=51:53, b=61:63)
cc <- list(gl(3,2), ca, cb, 91:94, short=81:82, letters[1:5])
asSepList(cc)
cd <- list(e1=gl(3,2), e2=ca, e3=cb, e4=91:94, short=81:82, e6=letters[1:5])
asSepList(cd)

Connect edges to from tree and extract all possible branches

Description

It is assumed that multiple fragments from a common ancestor bay be charcterized by the their start- and end-sites by integer values. For example, If 'abcdefg' is the ancestor, the fragments 'bcd' (from position 2 to 4) to and 'efg' may then be assembled. To do so, all fragments must be presented as matix specifying all start- and end-sites (and fragment-names). buildTree searchs contiguous fragments from columns 'posCo' (start/end) from 'disDat' to build tree & extract path information starting with line 'startFr'. Made for telling if dissociated fragments contribute to long assemblies. This function uses various functions of package data.tree which must be installed, too.

Usage

buildTree(
  disDat,
  startFr = NULL,
  posCo = c("beg", "end"),
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a list with $paths (branches as matrix with columns 'sumLen' & 'n'), $usedNodes (character vector of all names used to build tree) and $tree (object from data.tree)

See Also

package data.tree original function used Node; in this package : for exploiting edge/tree related issues simpleFragFig, countSameStartEnd and contribToContigPerFrag,

Examples

frag2 <- cbind(beg=c(2,3,7,13,13,15,7,9,7,3,7,5,7,3),end=c(6,12,8,18,20,20,19,12,12,4,12,7,12,4)) 
rownames(frag2) <- c("A","E","B","C","D","F","H","G","I", "J","K","L","M","N")
buildTree(frag2)
countSameStartEnd(frag2)

cbind to non-redundant

Description

cbindNR combines all matrixes given as arguments to non-redundant column names (by ADDING the number of 'duplicated' columns !). Thus, this function works similar to cbind, but allows combining multiple matrix-objects containing redundant column-names. Of course, all input-matrixes must have the same number of rows ! By default, the output gets sorted by column-names. Note, due to the use of '...' arguments must be given by their full argument-names, lazy evaluation might not recognize properly argument names.

Usage

cbindNR(
  ...,
  convertDFtoMatr = TRUE,
  sortOutput = TRUE,
  summarizeAs = "sum",
  silent = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a matrix or data.frame (as cbind would return)

See Also

cbind, nonAmbiguousNum, firstOfRepLines

Examples

ma1 <- matrix(1:6, ncol=3, dimnames=list(1:2,LETTERS[3:1]))
ma2 <- matrix(11:16, ncol=3, dimnames=list(1:2,LETTERS[3:5]))
cbindNR(ma1, ma2)
cbindNR(ma1, ma2, summarizeAs="mean")

Check how multiple groups of data separate or overlap based on mean +/- sd

Description

checkAvSd compares if/how neighbour groups separate/overlap via the 'engineering approach' (+/- 2 standard-deviations is similar to a=0.05 t.test). This approach may be used as less elegant alternative to (multi-group) logistic regression. The function uses 'daAv' as matrix of means (rows are tested for up/down character/progression) which get compared with boundaries taken from daSd (for Sd values of each mean in 'daAv').

Usage

checkAvSd(
  daAv,
  daSd,
  nByGr = NULL,
  multSd = 2,
  codeConst = "const",
  extSearch = FALSE,
  outAsLogical = TRUE,
  silent = FALSE,
  callFrom = NULL
)

Arguments

Value

vector describing character as 'const' or 'okUp','okDw' (or if extSearch=TRUE 'nearUp','nearDw')

See Also

rowGrpMeans

Examples

mat1 <- matrix(rep(11:24,3)[1:40],byrow=TRUE,ncol=8)
checkGrpOrderSEM(mat1,grp=gl(3,3)[-1])
checkAvSd(rowGrpMeans(mat1,gl(3,3)[-1]),rowGrpSds(mat1,gl(3,3)[-1]) )
# consider variable n :
checkAvSd(rowGrpMeans(mat1,gl(3,3)[-1]),rowGrpSds(mat1,gl(3,3)[-1]),nByGr=c(2,3,3)) 

Verify File-name If Existing (in specified path), If Has Proper Extension Or Select Files With Proper Extension From Given Path

Description

This function allows tesing if a given file-name corresponds to an existing file (eg for reading lateron). Indications to the path and file-extensions may be given separately. If no files do match .gz compressed versions may be searched, too.

Usage

checkFilePath(
  fileName = NULL,
  path = "./",
  expectExt = "",
  mode = "compressedOption",
  compressedOption = NULL,
  strictExtension = NULL,
  stopIfNothing = NULL,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Details

When the filename given by the user exists but it's file-extension is not matching expectExt the argument strictExtension allows to decide if the filename will still be returned or not.

When expectExt is given, initial search will look for perfect matches. However, if nothing is found and strictExtension=FALSE, a more relaxed and non-case-sensitive search will be performed.

Value

This function returns a character vector with verified file-name(s) (and path), returns NULL if nothing found - unless mode="stopIfNothing"

See Also

file.exists

Examples

(RhomeFi <- list.files(R.home()))
file.exists(file.path(R.home(), "bin"))
checkFilePath(c("xxx","unins000"), R.home(), expectExt="dat")

checkGrpOrder

Description

checkGrpOrder tests each line of 'x' if expected order appears. Used for comparing groups of measures with expected profile (simply by mataching expected order)

Usage

checkGrpOrder(
  x,
  rankExp = NULL,
  revRank = TRUE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

vector of logical values

See Also

checkGrpOrderSEM

Examples

set.seed(2005); mat1 <- rbind(matrix(round(runif(40),1),nc=4), rep(1,4))
checkGrpOrder(mat1)
checkGrpOrder(mat1,c(1,4,3,2))

Check order of multiple groups including non-overlapping SEM-margins

Description

checkGrpOrderSEM tests each line of 'x' if expected order of (replicate-) groups (defined in 'grp') appears intact, while inluding SEM of groups (replicates) via a proportional weight 'sdFact' as (avGr1-gr1SEM) < (avGr1+gr1SEM) < (avGr2-gr2SEM) < (avGr2+gr2SEM). Used for comparing groups of measures with expected profile (by matching expected order) to check if data in 'x' represting groups ('grp') as lines follow. Groups of size=1: The sd (and SEM) can't be estimated directly without any replicates, however, an estimate can be given by shrinking if 'shrink1sampSd'=TRUE under the hypothesis that the overall mechanisms determining the variances is constant across all samples.

Usage

checkGrpOrderSEM(
  x,
  grp,
  sdFact = 1,
  revRank = TRUE,
  shrink1sampSd = TRUE,
  silent = FALSE,
  callFrom = NULL
)

Arguments

Value

logical vector if order correct (as expected based on ranks)

See Also

takes only 10

Examples

mat1 <- matrix(rep(11:24,3)[1:40],byrow=TRUE,ncol=8)
checkGrpOrderSEM(mat1,grp=gl(3,3)[-1])

Check for similar values in series

Description

This function checks all values of 'x' for similar neighbour values within (relative) range of 'ppm' (ie parts per milion as measure of distance). By default values will be sorted internally, so if a given value of x has anywhere in x another value close enough, this will be detected. However, if sortX=FALSE only the values next to left and right will be considered. Return logical vector : FALSE for each entry of 'x' if value inside of ppm range to neighbour (of sorted values)

Usage

checkSimValueInSer(
  x,
  ppm = 5,
  sortX = TRUE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a logical vector : TRUE for each entry of x where at least one neighbour is inside of ppm distance/range

See Also

similar with more options withinRefRange

Examples

va1 <- c(4:7,7,7,7,7,8:10)+(1:11)/28600; checkSimValueInSer(va1)
data.frame(va=sort(va1),simil=checkSimValueInSer(va1))

Check for strict (ascencing or descending) order

Description

checkStrictOrder tests lines of 'dat' (matrix of data.frame) for strict order (ascending, descending or constant), each col of data is tested relative to the col on its left.

Usage

checkStrictOrder(
  dat,
  invertCount = FALSE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

matrix with counts of up pairs, down pairs, equal-pairs, if 'invertCount'=TRUE all non-events are counted, ie a resulting 0 means that all columns are following the described characteristics (with variabale col-numbers easier to count)

See Also

order, checkGrpOrder

Examples

set.seed(2005); mat1 <- rbind(matrix(round(runif(40),1),nc=4), rep(1,4))
checkStrictOrder(mat1); mat1[which(checkStrictOrder(mat1)[,2]==0),]

Check For Common Unit-Name in Text

Description

This function aims to find a unit abbreviation or name occurring in all elements of a character-vector x. The unit name may be preceeded by different decimal prefixes (eg 'k','M'), as defined by argument pref and separators (sep). The unit name will be returned (or first of multiple).

Usage

checkUnitPrefix(
  x,
  pref = c("a", "f", "p", "n", "u", "m", "", "k", "M", "G", "T", "P"),
  unit = c("m", "s", "sec", "Mol", "mol", "g", "K", "cd", "A", "W", "Watt", "V", "Volt"),
  sep = c("", " ", ";", ",", "_", "."),
  sep2 = "",
  stringentSearch = FALSE,
  na.rm = FALSE,
  protSpecChar = TRUE,
  inclPat = FALSE,
  callFrom = NULL,
  silent = FALSE,
  debug = FALSE
)

Arguments

Details

Basically this function searches the pattern : digit + separator(sep) + prefix(pref) + unit + optional separator2(sep2) and returns the first unit-name/abbreviation found in all elements of x.

If if() In case of invalid entries or no common unit-names NULL will be returned.

Please note the 'u' is used for 'micro' since handeling of special characters may not be portal between different operating systems.

Value

This function returns a charcter vector (length=1) with the common unit name, if inclPat=TRUE it returns a list with $unit and $pattern

See Also

convToNum; adjustUnitPrefix

Examples

x1 <- c("10fg WW","xx 10fg 3pW"," 1pg 2.0W")
checkUnitPrefix(x1)
## different separators between digit and prefix:
x2 <- c("10fg WW","xx 8_fg 3pW"," 1 pg-2.0W")
checkUnitPrefix(x2, stringentSearch=TRUE)
checkUnitPrefix(x2, stringentSearch=FALSE)

x4 <- c("CT_mixture_QY_50_amol_CN_UPS1_CV_Standards_Research_Group",
  "CT_mixture_QY_5_fmol_CN_UPS1_CV_Standards_Research_Group")

Check length of vector

Description

checkVectLength checks argument 'x' for expected length 'expeL' and return either message or error when expectation not met. May be used for parameter ('sanity') checking in other user front-end functions.

Usage

checkVectLength(
  x,
  expeL = 1,
  stopOnProblem = FALSE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns NULL; it produces either error-message if length is not OK or optional message if length is OK

Examples

aa <- 1:5; checkVectLength(aa,exp=3)

Choose Column Most Likely For Sample-Names

Description

This function looks at all comumns of mat which columns may be likely choices for sample-names and derives then group-names after stripping terminal enumerators. Ideal sample-names should contain some replicates indicates as terminal enumerators.

Usage

chooseGroupNames(
  mat,
  useCoNa = NULL,
  method = "median",
  sep = c("_", "-", " ", ".", "=", ";"),
  rmTxt = NULL,
  asUnique = TRUE,
  partEnumerator = FALSE,
  fullReport = FALSE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Details

The basic idea is that the column containing (good) samples-names contains all different entries and that by stripping terminal enumerators one can understand the grouping of replicates. Note arguments asUnique and partEnumerator influence which columns of mat will be evaluated/checked

Value

This function returns a character vector with grouop-names (and sample-names as names of entries) or if fullReport=TRUE a list with $group, $sampleNames, $col (index of column from mat and name of column

See Also

rmSharedWords, replicateStructure, protectSpecChar

Examples

 mat <- cbind(a=letters[1:6], b=paste(rep(c("b","B"), each=3), 1:3), c=rep(1,6), 
   d=gl(3,2), e=rep(c("e","E"),3), f=paste(rep(c("F","f","ff"), each=2), 1:2))
chooseGroupNames(mat, method="median")         # col 2 (b/B)
chooseGroupNames(mat, method="median", fullReport=TRUE) 
chooseGroupNames(mat, method="min")            # col 2 (b/B)
chooseGroupNames(mat, method="max")            # col 6 (F/f/ff)
chooseGroupNames(mat, method="max", asUnique=FALSE) # col 1 (a..)

Replace Most Distant Values by NA

Description

This procedures aims to streighten (clean) the most extreme values of noisy replicates by identifying the most distant points (among a set of replicates). The input 'x' (matrix or data.frame) is supposed to come from multiple different measures taken in replicates (eg weight of different individuals as rows taken as multiple replicate measures in subsequent columns).

Usage

cleanReplicates(
  x,
  centrMeth = "median",
  nOutl = 2,
  retOffPos = FALSE,
  silent = FALSE,
  callFrom = NULL
)

Arguments

Details

With the argument nOutl the user chooses the total number of most extreme values to replace by NA. how many of the most extreme replicates of the whole dataset will replaced by NA, ie with nOutl=1 only the single most extreme outlyer will be replaced by NA. Outlier points are determined as point(s) with highest distance to (row) center (median and mean choice via argument 'centrMeth'). Thus function returns input data with "removed" points set to NA, or if retOffPos=TRUE the most extreme/outlier positions.

Value

This function returns a matrix of same dimensions as input x, data-points which were tagged/removed are set to NA, or if retOffPos=TRUE the most extreme/outlier positions

Examples

mat3 <- matrix(c(19,20,30, 18,19,28, 16,14,35),ncol=3)
cleanReplicates(mat3, nOutl=1)

Reorganize results of search for close (similar) values in matrix-view

Description

closeMatchMatrix reorganizes/refines results from simple search of similar values of 2 sets of data by findCloseMatch (as list for one-to many relations) to more human friendly/readable matrix. This function returns results combining two sets of data which were initially compared (eg measured and threoretical values) as matrix-view using output of findCloseMatch and both original datastes Additional information (covariables, annotation, ...) may be included as optional columns for either 'predMatr' or 'measMatr'. Note : It is important to run findCloseMatch with sortMatch=FALSE ! Note : Results presented based on view of 'predMatr', so if multiple 'measMatr' are at within tolared distance, lines of 'measMatr' will be repeated; Note : Distances 'disToMeas' and 'ppmToPred' are oriented : neg value if measured is lower than predicted (and pos values if higher than predicted); Note : Returns NULL when nothing within given limits of comparison;

Usage

closeMatchMatrix(
  closeMatch,
  predMatr,
  measMatr,
  prefMatch = c("^x", "^y"),
  colPred = 1,
  colMeas = 1,
  limitToBest = TRUE,
  asDataFrame = FALSE,
  origNa = TRUE,
  silent = FALSE,
  callFrom = NULL,
  debug = FALSE
)

Arguments

Value

results as matrix-view based on initial results from findCloseMatch, including optional columns of suppelemental data for both sets of data for comparison. Returns NULL when nothing within limits

See Also

findCloseMatch, checkSimValueInSer

Examples

aA <- c(11:17); bB <- c(12.001,13.999); cC <- c(16.2,8,9,12.5,15.9,13.5,15.7,14.1,5)
(cloMa <- findCloseMatch(aA,cC,com="diff",lim=0.5,sor=FALSE))       
# all matches (of 2d arg) to/within limit for each of 1st arg ('x'); 'y' ..to 2nd arg = cC
(maAa <- closeMatchMatrix(cloMa,aA,cC,lim=TRUE))  #
(maAa <- closeMatchMatrix(cloMa,aA,cC,lim=FALSE,origN=TRUE))  #
(maAa <- closeMatchMatrix(cloMa,cbind(valA=81:87,aA),cbind(valC=91:99,cC),colM=2,
  colP=2,lim=FALSE))
(maAa <- closeMatchMatrix(cloMa,cbind(aA,valA=81:87),cC,lim=FALSE,deb=TRUE))  #
a2 <- aA; names(a2) <- letters[1:length(a2)];  c2 <- cC; names(c2) <- letters[10+1:length(c2)]
(cloM2 <- findCloseMatch(x=a2,y=c2,com="diff",lim=0.5,sor=FALSE)) 
(maA2 <- closeMatchMatrix(cloM2,predM=cbind(valA=81:87,a2),measM=cbind(valC=91:99,c2),
  colM=2,colP=2,lim=FALSE,asData=TRUE)) 
(maA2 <- closeMatchMatrix(cloM2,cbind(id=names(a2),valA=81:87,a2),cbind(id=names(c2),
  valC=91:99,c2),colM=3,colP=3,lim=FALSE,deb=FALSE)) 

Compare Means Of Two Vectors By Permutation Test

Description

Run coin-flipping like permutation tests (to compare difference of 2 means: 'x1' and 'x2') without any distribution-assumptions. This function uses the package coin, if not installed, the function will return NULL and give a warning.

Usage

coinPermTest(
  x1,
  x2,
  orient = "two.sided",
  nPerm = 5000,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns an object of "MCp" class numeric output with p-values

See Also

oneway_test in LocationTests

Examples

coinPermTest(2, 3, nPerm=200)

rowCVs

Description

This function returns CV for values in each column (using speed optimized standard deviation). Note : NaN values get replaced by NA.

Usage

colCVs(dat, autoconvert = NULL, silent = FALSE, debug = FALSE, callFrom = NULL)

Arguments

Value

This function returns a (numeric) vector with CVs for each column of 'dat'

See Also

rowSums, rowCVs, rowGrpCV, colSds

Examples

set.seed(2016); dat1 <- matrix(c(runif(200) +rep(1:10,20)),ncol=10)
head(colCVs(dat1))

Standard Error Of Median For Each Column By Bootstrap

Description

Determine standard error (sd) of median by bootstraping for multiple sets of data (rows in input matrix 'dat'). Note: The package boot must be installed from CRAN.

Usage

colMedSds(dat, nBoot = 99, silent = FALSE, debug = FALSE, callFrom = NULL)

Arguments

Value

This function returns a (numeric) vector with estimated standard errors

See Also

boot

Examples

set.seed(2016); dat1 <- matrix(c(runif(200) +rep(1:10,20)), ncol=10)
colMedSds(dat1) 

sd for each column

Description

This function is speed optimized sd per column of a matrix or data.frame and treats each column as independent set of data for sd (equiv to apply(dat,2,sd)). NAs are ignored from data. Speed improvements may be seen at more than 100 columns

Usage

colSds(dat, silent = FALSE, debug = FALSE, callFrom = NULL)

Arguments

Value

numeric vector of sd values

See Also

sd

Examples

set.seed(2016); dat1 <- matrix(c(runif(200) +rep(1:10,20)), ncol=10)
colSds(dat1)

Transform Numeric Values To Color-Gradient

Description

This function helps making color-gradients for plotting a numerical variable. Note : RColorBrewer palettes were not integrated here/yet.

Usage

colorAccording2(
  x,
  gradTy = "rainbow",
  nStartOmit = NULL,
  nEndOmit = NULL,
  revCol = FALSE,
  alpha = 1,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a character vector (of same length as x) with color encoding

See Also

cut

Examples

set.seed(2015); dat1 <- round(runif(15),2)
plot(1:15,dat1,pch=16,cex=2,col=colorAccording2(dat1))
plot(1:15,dat1,pch=16,cex=2,col=colorAccording2(dat1,nStartO=0,nEndO=4,revCol=TRUE))
plot(1:9,pch=3)
points(1:9,1:9,col=transpGraySca(st=0,en=0.8,nSt=9,trans=0.3),cex=42,pch=16)

Planing for making all multiplicative combinations

Description

Provide all combinations for each of n elements of vector 'nMax' (positive integer, eg number of max multiplicative value). For example, imagine, we have 3 cities and the (maximum) voting participants per city. Results must be read vertically and allow to see all total possible compositons.

Usage

combinatIntTable(
  nMax,
  include0 = TRUE,
  asList = FALSE,
  callFrom = NULL,
  silent = TRUE
)

Arguments

Value

list or array (as 2- or 3 dim) with possible number of occurances for each of the 3 elements in nMax. Read results vertical : out[[1]] or out[,,1] .. (multiplicative) table for 1st element of nMax; out[,,2] .. for 2nd

See Also

combn

Examples

combinatIntTable(c(1,1,1,2), include0=TRUE, asList=FALSE, silent=TRUE)
## Imagine we have 3 cities and the (maximum) voting participants per city :
nMa <- c(Paris=2, Lyon=1, Strasbourg=1)
combinatIntTable(nMa, include0=TRUE, asList=TRUE, silent=TRUE) 

Combine Vectors From List And Return Basic Count Statistics

Description

The aim of this function is to choose a fixed number (nCombin) of list-elments from lst and count the number of common values/words. Furthermore, one can define levels to fine-tune the types of combinations to examine. In case multiple combinations for a given level are possible, some basic summary statistics are provided, too.

Usage

combineAsN(
  lst,
  lev = NULL,
  nCombin = 3,
  remDouble = TRUE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Details

Note of caution : With very long lists and/or high numbers of repeats of given levels, however, the computational effort incerases very much (like it does when using table). Thus, when exploring all different combinations of large data-sets may easily result in queries consuming many ressources (RAM and processing time) ! It is recommended to start testing with test smaller sub-groups.

The main idea of this function is to count frequency of terms when combining different drawings. For example, you ask students from different cities which are their preferred hobbies, they may have different preference depending on the city ( defined by lev). Now, if you want to make groups of 3 students, possibly with one from each city (A ,B and C), you want to count (/estimate) the frequency of different combinations possible. Thus, using this function all combinations of the students from city A with the students from city B and C will be made when counting the number of common hobbies (by nCombin students). Then, all counting results will be summarized to the average count for the various categories (which hobbies were seen once, twice or 3 times...), sem (standard error of the mean) and CI (95

Of course, the number of potential combinations may quickly get very large. Using the argument remDouble=TRUE you can limit the search to either finding all students giving the same answer plus all student giving different answers. In this case, when a given level appears multiple times, all possible combinations using one of the respective entries will be be made with the other levels.

Value

This function returns an array with 3 dimensions : i) ii) the combinations of nCombin list-elements, iii) the number of counts (n), sem (standard error of the mean), CI (confidence interval) and sd

See Also

table, replicateStructure

Examples

## all list-elements are considered equal
tm1 <- list(a1=LETTERS[1:17], a2=LETTERS[3:19], a3=LETTERS[6:20], a4=LETTERS[8:22])
combineAsN(tm1, lev=gl(1,4))[,1,]

## different levels/groups in list-elements
tm4 <- list(a1=LETTERS[1:15], a2=LETTERS[3:16], a3=LETTERS[6:17], a4=LETTERS[8:19],
  b1=LETTERS[5:19], b2=LETTERS[7:20], b3=LETTERS[11:24], b4=LETTERS[13:25], c1=LETTERS[17:26],
  d1=LETTERS[4:12], d2=LETTERS[5:11], d3=LETTERS[6:12], e1=LETTERS[7:10])
te4 <- combineAsN(tm4, nCombin=4, lev=substr(names(tm4),1,1))
str(te4)
te4[,,1]

Create factor-like column regrouping data regrouping simultaneaously by two factors

Description

This function aims to address the situation when two somehow different groupins (of the same data) exist and need to be joined. It is not necessary that both alternative groupings use the same labels, neither. combineByEitherFactor adds new (last) column named 'grp' to input matrix representing the combined factor relative to 2 specified columns from input matrix mat (via 'refC1','refC2'). Optionally, the output may be sorted and a column giving n per factor-level may be added. The function treats selected columns of mat as pairwise combination of 2 elements (that may occur multiple times over all lines of mat) and sorts/organizes all instances of such combined elements (ie from both selected columns) as repeats of a given group, who's class number is given in output column 'grp', the (total) number of repeats may be displayed in column 'nGrp' ( nByGrp=TRUE). If groups are overlapping (after re-ordering), an iterative process of max 3x2 passes will be launched after initial matching. Works on numeric as well as character input.

Usage

combineByEitherFactor(
  mat,
  refC1,
  refC2,
  nByGrp = FALSE,
  convergeMax = TRUE,
  callFrom = NULL,
  debug = FALSE,
  silent = FALSE
)

Arguments

Value

This function returns a matrix containing both selected columns plus additional column(s) indicating group-number of the pair-wise combination (and optional the total n by group)

Examples

nn <- rep(c("a","e","b","c","d","g","f"),c(3,1,2,2,1,2,1))
qq <- rep(c("m","n","p","o","q"),c(2,1,1,4,4))
nq <- cbind(nn,qq)[c(4,2,9,11,6,10,7,3,5,1,12,8),]
combineByEitherFactor(nq,1,2,nBy=TRUE); combineByEitherFactor(nq,1,2,nBy=FALSE)
combineByEitherFactor(nq,1,2,conv=FALSE); combineByEitherFactor(nq,1,2,conv=TRUE)
##
mm <- rep(c("a","b","c","d","e"),c(3,4,2,3,1)); pp <- rep(c("m","n","o","p","q"),c(2,2,2,2,5))
combineByEitherFactor(cbind(mm,pp), 1, 2, con=FALSE, nBy=TRUE)
combineByEitherFactor(cbind(mm,pp), 1, 2, con=TRUE, nBy=TRUE) 

Find and combine points located very close in x/y space

Description

Search points in x,y space that are located very close and thus likely to overlap. In case of points close enough, various options for joining names (and shortening longer descriptions) are available.

Usage

combineOverlapInfo(
  dat,
  suplInfo = NULL,
  disThr = 0.01,
  addNsimil = TRUE,
  txtSepChar = ",",
  combSym = "+",
  maxOverl = 50,
  callFrom = NULL,
  debug = FALSE,
  silent = FALSE
)

Arguments

Value

matrix with fused (condensed) information for cluster of overapping points

Examples

set.seed(2013)
datT2 <- matrix(round(rnorm(200)+3,1),ncol=2,dimnames=list(paste("li",1:100,sep=""),
  letters[23:24]))
# (mimick) some short and longer names for each line
inf2 <- cbind(sh=paste(rep(letters[1:4],each=26),rep(letters,4),1:(26*4),sep=""),
 lo=paste(rep(LETTERS[1:4],each=26),rep(LETTERS,4),1:(26*4),",",rep(letters[sample.int(26)],4),
  rep(letters[sample.int(26)],4),sep=""))[1:100,] 
head(datT2,n=10)
head(combineOverlapInfo(datT2,disThr=0.03),n=10)
head(combineOverlapInfo(datT2,suplI=inf2[,2],disThr=0.03),n=10)

Combine/reduce redundant lines based on specified column

Description

This function works similar to unique, but it takes a matrix as input and considers one specified column to find unique instances. It identifies 'repeated' lines of the input-matrix (or data.frame) 'mat' based on (repeated) elements in/of column with name 'colNa' (or column-number). Redundant lines (ie repeated lines) will disappear in output. Eg used with extracted annotation where 1 gene has many lines for different GO annotation.

Usage

combineRedBasedOnCol(mat, colNa, sep = ",", silent = FALSE, callFrom = NULL)

Arguments

Value

matrix containing the input matrix without lines considered repeated (unique-like)

See Also

findRepeated, firstOfRepLines, organizeAsListOfRepl, combineRedundLinesInList

Examples

matr <- matrix(c(letters[1:6],"h","h","f","e",LETTERS[1:5]),ncol=3,
  dimnames=list(letters[11:15],c("xA","xB","xC")))
combineRedBasedOnCol(matr,colN="xB")
combineRedBasedOnCol(rbind(matr[1,],matr),colN="xB")

Combine Redundant Lines In List

Description

This function provides help for combining/summarizing lines of numeric data which may be summaried according to reference vector or matrix of annotation (part of the same input-list). The data and reference will be aligned and data corresponding to redundant information be combined/summarized.

Usage

combineRedundLinesInList(
  lst,
  refNa = "ref",
  datNa = "quant",
  refColNa = "GeneName",
  supRefColNa = NULL,
  summarizeType = "av",
  NA.rm = TRUE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Details

All input data should be in a list, ie one or multipl matrix or data.frame for numeric data (see argument datNa), as well as the reference (see argument refNa). The refgerence may be a named character vecor or a matrix for which the column to be used should be specified using the argument refColNa. In case the annotation is a matrix, the rownames will be used as unique/independent identifyers to adjust potentially different order of numeric data and annotation. In absence of rownames, an additional column supRefColNa of the annotation may be designed for adjusting the order of annotation and numeric data.

The numeric list may contain multiple matrixes or data.frames which will all be summarized by the same procedure as long as they have the same initial dimensions and are specified by refNa.

Please note that all other list elements from input not specified by refNa (or datNa) will be maintained in the output just as they are.

Value

This function returns a list of same length as input

See Also

findRepeated, firstOfRepLines, organizeAsListOfRepl, combineRedBasedOnCol

Examples

x1 <- list(quant=matrix(11:34, ncol=3, dimnames=list(letters[8:1], LETTERS[11:13])), 
  annot=matrix(paste0(LETTERS[c(1:4,6,3:5)],LETTERS[c(1:4,6,3:5)]), ncol=1, 
  dimnames=list(paste(letters[1:8]),"xx")) )
combineRedundLinesInList(lst=x1, refNa="annot", datNa="quant", refColNa="xx")

Combine Redundant Lines In List, Deprecated

Description

The function combineRedundLinesInListAcRef() has been deprecated and replaced by combineRedundLinesInList() from the same package

Usage

combineRedundLinesInListAcRef(
  lst,
  listNa = c("ref", "quant"),
  refColNa = "xx",
  summarizeType = "av",
  NA.rm = TRUE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a list of same length as input

See Also

combineRedundLinesInList

Examples

x1 <- list(quant=matrix(11:34, ncol=3, dimnames=list(letters[8:1], LETTERS[11:13])), 
  annot=matrix(paste0(LETTERS[c(1:4,6,3:5)],LETTERS[c(1:4,6,3:5)]), ncol=1, 
  dimnames=list(paste(letters[1:8]),"xx")) )
## please use combineRedundLinesInList()
combineRedundLinesInList(lst=x1, refNa="annot", datNa="quant", refColNa="xx")

Combine replicates from list to matrix

Description

Suppose multiple measures (like multiple chanels) are taken for subjects and these measures are organized as groups in a list, like muliple parameters (= channels) or types of measurements (typically many paramters are recorded when screeinig compounds in microtiter plates). Within one parameter/channel all replicate-data from separate list-entries ('lst') will get combined according to names of list-elements. The function will trim any redundant text in names of list-elements, try to isolate separator (may vary among replicate-groups, but should be 1 character long). eg names "hct116 1.1.xlsx" & "hct116 1.2.xlsx" will be combined as replicates, "hct116 2.1.xlsx" will be considered as new group.

Usage

combineReplFromListToMatr(lst, silent = FALSE, debug = FALSE, callFrom = NULL)

Arguments

Value

list of arrays now with same dimension of arrays (but shorter, since replicate-arrays were combined)

See Also

extr1chan, organizeAsListOfRepl

Examples

lst2 <- list(aa_1x=matrix(1:12,nrow=4,byrow=TRUE),ab_2x=matrix(24:13,nrow=4,byrow=TRUE))
combineReplFromListToMatr(lst2)

Get all combinations with TRUE from each column

Description

This function addresses the case when multiple alternatove ways exit to combine two elements. combineSingleT makes combinatory choices : if multiple TRUE in given column of 'mat' make all multiple selections with always one TRUE from each column The resultant output contains index for first and second input columns elements to be combined.

Usage

combineSingleT(mat)

Arguments

Value

matrix with indexes of conbinations of TRUE

Examples

## Example: Fist column indicates which boys want to dance and second column  
## which girls want to dance. So if several boys want to dance each of the girls 
## will have the chance to dance with each of them.  
matr <- matrix(c(TRUE,FALSE,TRUE,FALSE,TRUE,FALSE),ncol=2)
combineSingleT(matr)

Complete list of arrays for same dimensions

Description

This functions aims to inspect repeating structues of data given as list of arrays and will try to complete arrays with fewer lines or columns (as this may appear eg with the very last set of high-thourput sceening data if fewer measures remain in the last set). Thus, the dimensions of the arrays are compared and cases with fewer (lost) columns (eg fewer experimental replicates) will be adjust/complete by adding column(s) of NA. Used eg when at reading mircotiterplate data the last set is not complete.

Usage

completeArrLst(arrLst, silent = FALSE, callFrom = NULL)

Arguments

Value

list of arrays, now with same dimension of arrays

See Also

organizeAsListOfRepl, extr1chan

Examples

arr1 <- array(1:24,dim=c(4,3,2),dimnames=list(c(LETTERS[1:4]),
  paste("col",1:3,sep=""),c("ch1","ch2")))
arr3 <- array(81:96,dim=c(4,2,2),dimnames=list(c(LETTERS[1:4]),
  paste("col",1:2,sep=""),c("ch1","ch2")))
arrL3 <- list(pl1=arr1,pl3=arr3)
completeArrLst(arrL3)

Value Matching With Option For Concatenated Terms

Description

This is a _match()_-like function allowing to serach among concatenated terms/IDs, additional options to remove text pattern like terminal lowercase extesion are available. The function returns a named vector indicating the positions of (first) matches similar to match.

Usage

concatMatch(
  x,
  table,
  sep = ",",
  sepPattern = NULL,
  globalPat = "digitExtension",
  nomatch = NA_integer_,
  incomparables = NULL,
  extensPat = TRUE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Details

The main motivation to create this function was to be able to treat concatenated entries and to look if any of the concatenated values match to 'x'. This function offers additional options for trimming values before running the main comparison.

Of course, the concatenation strategy must be known and only a single concatenation separator (which may be multiple characters long) may be used for both x and match. Thus result will only indicate that at least one of the concatenated terms had a match, but not which one. Finally, both vectors x and table may contain concatenated terms. In this case this function will require much more computational ressources due to the increased combinatorics when comparing larger vectors.

Please note, that in case of multiple to multiple matches, only the first hit gets reported.

The argument globalPat="digitExtension" allows eg reducing 'A1234-4' to 'A1234'.

Value

This function returns a character vector with verified path and file-name(s), returns NULL if nothing

See Also

match (for two simple vectors without concatenated terms), grep

Examples

tab1 <- c("AA","BB-5","CCab","FF")
tab2 <- c("AA","WW,Vde,BB-5,E","CCab","FF,Uef")
x1 <- c("ZZ","YY","AA","BB-2","DD","CCdef","Dxy")            # modif of single ID (no concat)
concatMatch(x1, tab2)
x2 <- c("ZZ,Z","YY,Y","AA,Z,Y","BB-2","DD","X,CCdef","Dxy")  # conatenated in 'x'
concatMatch(x2, tab2)
tab1 <- c("AA","BB-5","CCab","FF")              # no conatenated in 'table'
concatMatch(x2, tab1)                          # simple case of no concat anywhere
concatMatch(x1, tab1)

Confidence Interval To Given Alpha

Description

This little function returns the confidence interval associated to a given significance level alpha under the hypothesis of the Normal distribution is valid.

Usage

confInt(x, alpha = 0.05, distrib = "Normal", silent = FALSE, callFrom = NULL)

Arguments

Value

This function returns the confidence interval to a given alpha under the hypothesis of the Normal distribution.

See Also

TDist; confint

Examples

confInt(c(5,2:6))

Characterize individual contribution of single edges in tree-structures

Description

This function helps investigating tree-like structures with the aim of indicating how much individual tree components contribute to compose long stretches. contribToContigPerFrag characterizes individual (isolated) contribution of single edges in tree-structures. Typically used to process/exploit summarized trees (as matrix) made by buildTree which makes use of the package data.tree. For example if A,B and C can be joined aa well and B +D, this function will check if A+B+C is longer and if A contributes to the longest tree.

Usage

contribToContigPerFrag(joinMat, fullLength = NULL, nDig = 3)

Arguments

Value

matrix of 3 columns: with length of longest tree-branches where given edge participates (column sumLen), the (total) number of edges therein (col n.frag) and a relative value (len.rat)

See Also

to build tree buildTree

Examples

path1 <- matrix(c(17,19,18,17, 4,4,2,3),ncol=2,
  dimnames=list(c("A/B/C/D","A/B/G/D","A/H","A/H/I"),c("sumLen","n")))
contribToContigPerFrag(path1)

Convert matrix of integer to matrix of x-times repeated column-names

Description

conv01toColNa transforms matrix of integers (eg 0 and 1) to repeated & concatenated text from argument colNa, the character string for 0 occurances of argument zeroTex may be customized. Used eg when specifying (and concatenating) various counted elements (eg properties) along a vector like variable peptide modifications in proteomics.

Usage

conv01toColNa(mat, colNa = NULL, zeroTex = "", pasteCol = FALSE)

Arguments

Value

character vector

Examples

(ma1 <- matrix(sample(0:3,40,repl=TRUE), ncol=4, dimnames=list(NULL, letters[11:14])))
conv01toColNa(ma1)
conv01toColNa(ma1, colNa=LETTERS[1:4], ze=".")
conv01toColNa(ma1, colNa=LETTERS[1:4], pasteCol=TRUE)

Assign new transparency to given colors

Description

This function alows (re-)defining a new transparency. A color encoding vector will be transformed to the same color(s) but with new transparency (alpha).

Usage

convColorToTransp(color, alph = 1)

Arguments

Value

character vector (of same length as input) with color encoding for new transparency

See Also

rgb, par

Examples

col0 <- c("#998FCC","#5AC3BA","#CBD34E","#FF7D73")
col1 <- convColorToTransp(col0,alph=0.7)
layout(1:2)
pie(rep(1,length(col0)),col=col0)
pie(rep(1,length(col1)),col=col1,main="new transparency")

Convert matrix (eg with redundant) row-names to data.frame

Description

This function provides flexible converting of matrix to data.frame. For example repeated/redundant rownames are not allowed in data.frame(), thus the corresponding column-names have to be renamed using a counter-suffix. In case of non-redundant rownames, a new column 'addIniNa' will be introduced at beginning to document the initial (redundant) rownames, non-redundant rownames will be created. Finally, this functions converts the corrected matrix to data.frame and checks/converts columns for transforming character to numeric if possible. If the input is a data.frame containing factors, they will be converted to character before potential conversion. Note: for simpler version (only text to numeric) see from this package .convertMatrToNum .

Usage

convMatr2df(
  mat,
  addIniNa = TRUE,
  duplTxtSep = "_",
  silent = FALSE,
  callFrom = NULL
)

Arguments

Value

This functions returns a data.frame equivalent to the input matrix, an additional column named 'ID' will be added for initial rownames

See Also

numeric, for simpler version (only text to numeric) see from this package .convertMatrToNum

Examples

dat1 <- matrix(1:10, ncol=2)
rownames(dat1) <- letters[c(1:3,2,5)]
## as.data.frame(dat1)  ...  would result in an error
convMatr2df(dat1)

df1 <- data.frame(a=as.character((1:3)/2), b=LETTERS[1:3], c=1:3)
str(convMatr2df(df1))

df2 <- df1; df2$b <- as.factor(df2$b)
str(convMatr2df(df2))

Convert Vector To Numeric

Description

This function checks if input vector/character string contains numbers (with or without comma) and attempts converting to numeric. This functions was designed for extracting the numeric part of character-vectors (or matrix) containing both numbers and character-elements. Depending on the parameters convert and remove text-entries can be converted to NA (in resulting numeric objects) or removed (the number of elements/lines gets reduced, in consequece). Note: if 'x' is a matrix, its matrix-dimensions & -names will be preserved. Note: so far Inf and -Inf do not get recognized as numeric.

Usage

convToNum(
  x,
  autoConv = TRUE,
  spaceRemove = TRUE,
  convert = c(NA, "sparseChar"),
  remove = NULL,
  euroStyle = TRUE,
  sciIncl = TRUE,
  callFrom = NULL,
  silent = TRUE,
  debug = FALSE
)

Arguments

Details

This function may be used in two modes, depening if argument autoConv is TRUE or FALSE. The first options allows accessing an automatic mode based on as.numeric, while the second options investigates all characters if they may belong to numeric expressions and allows removing specific text-elements.

Value

This function returns a numeric vector (or matrix (if 'x' is matrix))

See Also

numeric and as.numeric (on same help-page)

Examples

x1 <- c("+4"," + 5","6","bb","Na","-7") 
convToNum(x1) 
convToNum(x1, autoConv=FALSE, convert=c("allChar"))
convToNum(x1, autoConv=FALSE)      # too many non-numeric instances for 'sparseChar'

x2 <- c("+4"," + 5","6","-7"," - 8","1e6","+ 2.3e4","-3E4","- 4E5") 
convToNum(x2) 
convToNum(x2, autoConv=FALSE, convert=NA,remove=c("allChar",NA))
convToNum(x2, autoConv=FALSE, convert=NA,remove=c("allChar",NA),sciIncl=FALSE)

get coordinates of values/points in matrix according to filtering condition

Description

Get coordinates of values/points in matrix according to filtering condition

Usage

coordOfFilt(mat, cond, sortByRows = FALSE, silent = FALSE, callFrom = NULL)

Arguments

Value

matrix columns 'row' and 'col'

See Also

which

Examples

set.seed(2021); ma1 <- matrix(sample.int(n=40,size=27,replace=TRUE), ncol=9)
## let's test which values are >37
which(ma1 >37)      # doesn't tell which row & col
coordOfFilt(ma1, ma1 >37)

Correct vector to unique

Description

correctToUnique checks 'x' for unique entries, while maintaining the original length. If necessary a counter will added to non-unique entries.

Usage

correctToUnique(
  x,
  sep = "_",
  atEnd = TRUE,
  maxIter = 4,
  NAenum = TRUE,
  silent = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a character vector

See Also

unique will simply remove repeated elements, ie length of 'x' won't remain constant, filtSizeUniq is more complex and slower, treatTxtDuplicates

Examples

correctToUnique(c("li0","n",NA,NA,rep(c("li2","li3"),2),rep("n",4))) 

Correct mixed slash and backslash in file path

Description

This function corrects paths character strings for mixed slash and backslash in file path. In Windows the function tempdir() will use double backslashes as separator while file.path() uses regular slashes. So when combining these two one might encounter a mix of slashes and double backslashes which may cause trouble, unless this is streightened out to a single separator used. When pointig to given files inside html-files, paths need to have a prefix, this can be added using the argument asHtml.

Usage

correctWinPath(
  x,
  asHtml = FALSE,
  anyPlatf = FALSE,
  silent = TRUE,
  callFrom = NULL
)

Arguments

Value

character vector with corrected path

See Also

tempfile, file.path

Examples

path1 <- 'D:\\temp\\Rtmp6X8/working_dir\\RtmpKC/example.txt'
(path1b <- correctWinPath(path1, anyPlatf=TRUE)) 
(path1h <- correctWinPath(path1, anyPlatf=TRUE, asHtml=TRUE)) 

Count From Two Vectors Number Of Values Close Within Given Limits

Description

This functions summarizes the serach of similar (or identical) numeric values from 2 initial vectors, it evaluates the result from initial search run by findCloseMatch(), whose output is a less convenient list. countCloseToLimits checks furthermore how many results within additional (more stringent) distance-limits may be found and returns the number of distance values within the limits tested. Designed for checking if threshold used with findCloseMatch() may be set more stringent, eg when searching reasonable FDR limits ...

Usage

countCloseToLimits(
  closeMatch,
  limitIdent = 5,
  prefix = "lim_",
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns an integer vector with counts for number of list-elements with at least one absolue value below threshold, names

See Also

findCloseMatch

Examples

set.seed(2019); aa <- sample(12:15,20,repl=TRUE) +round(runif(20),2)-0.5
bb <- 11:18
match1 <- findCloseMatch(aa,bb,com="diff",lim=0.65)
head(match1)
(tmp3 <- countCloseToLimits(match1,lim=c(0.5,0.35,0.2)))
(tmp4 <- countCloseToLimits(match1,lim=0.7))

Count same start- and end- sites of edges (or fragments)

Description

Suppose a parent sequence/string 'ABCDE' gets cut in various fragments (eg 'ABC','AB' ...). countSameStartEnd counts how many (ie re-occuring) start- and end- sites of edges do occur in the input-data. The input is presented as matrix of/indicating start- and end-sites of edges. The function is used to characterize partially redundant edges and accumulation of cutting/breakage sites.

Usage

countSameStartEnd(frag, minFreq = 2, nDig = 4)

Arguments

Value

matrix of 6 columns: input (beg and end), beg.n, beg.rat, end.n, end.rat

See Also

to build initial tree buildTree, contribToContigPerFrag, simpleFragFig

Examples

frag1 <- cbind(beg=c(2,3,7,13,13,15,7,9,7, 3,3,5), end=c(6,12,8,18,20,20,19,12,12, 4,5,7))
rownames(frag1) <- letters[1:nrow(frag1)]
countSameStartEnd(frag1)
simpleFragFig(frag1)

Cut 3-dim array in list of matrixes (or arrays) similar to organizing into clusters

Description

cutArrayInCluLike cuts 'dat' (matrix,data.frame or 3-dim array) in list (of appended lines) according to 'cluOrg', which serves as instruction which line of 'dat' should be placed in which list-element (like sorting according to cluster-numbers).

Usage

cutArrayInCluLike(dat, cluOrg, silent = FALSE, debug = FALSE, callFrom = NULL)

Arguments

Value

This function retruns a list of matrixes (or arrays)

Examples

mat1 <- matrix(1:30,nc=3,dimnames=list(letters[1:10],1:3))
cutArrayInCluLike(mat1,cluOrg=factor(c(2,rep(1:4,2),5)))

Cut character-vector at multiple sites

Description

This function cuts character vector after 'cutAt' (ie keep the search subtsting 'cutAt', different to strsplit). Used for theoretical enzymatic digestion (eg in proteomics)

Usage

cutAtMultSites(y, cutAt)

Arguments

Value

modified (ie cut) character vector

See Also

strsplit, nFragments0, nFragments

Examples

tmp <- "MSVSRTMEDSCELDLVYVTERIIAVSFPSTANEENFRSNLREVAQMLKSKHGGNYLLFNLSERRPDITKLHAKVLEFGWPDLHTPALEKI"
cutAtMultSites(c(tmp,"ojioRij"),c("R","K"))

Cut numeric vector to n groups (ie convert to factor)

Description

cutToNgrp is a more elaborate version of cut for cutting a the content of a numeric vector 'x' into a given number of groups, taken from the length of 'lev'. Besides, this function provides the group borders/limits for convention use with legends.

Usage

cutToNgrp(x, lev, NAuse = FALSE, callFrom = NULL)

Arguments

Value

list with $grouped telling which element of 'x' goes in which group and $legTxt with gourp-borders for convenient use with legends

See Also

cut

Examples

set.seed(2019); dat <- runif(30) +(1:30)/2
cutToNgrp(dat,1:5)
plot(dat,col=(1:5)[as.numeric(cutToNgrp(dat,1:5)$grouped)])

Compute matrix of differences for all pairwise combinations of numeric vector

Description

diffCombin returns matrix of differences (eg resulting from subsititution) for all pairwise combinations of numeric vector 'x'.

Usage

diffCombin(x, diagAsNA = FALSE, prefix = TRUE, silent = FALSE, callFrom = NULL)

Arguments

Value

numeric matrix of all pairwise differences

See Also

diff for simple differences

Examples

diffCombin(c(10,11.1,13.3,16.6))

Difference in ppm between numeric values

Description

This is a diff()-like function to return difference in ppm between subsequent values. Result is oriented, ie neg ppm value means decrease (from higher to lower value). Note that if the absolute difference remains the same the difference in ppm will not remain same. Any difference to NA is returned as NA, thus a single NA will result in two NAs in output (unless NA is 1st or last).

Usage

diffPPM(dat, toPrev = FALSE, silent = FALSE, callFrom = NULL)

Arguments

Value

This function returns a list with close matches of 'x' to given 'y', the numeric value dependes on 'sortMatch' (if FALSE then always value of 'y' otherwise of longest of x&y)

See Also

checkSimValueInSer and (from this package) .compareByDiff, diff

Examples

aa <- c(1000.01, 1000.02, 1000.05, 1000.08, 1000.09, 1000.08)
.compareByPPM(list(aa,aa), 30, TRUE)                    # tabular 'long' version
diffPPM(aa)

Eliminate close (overlapping) points (in x & y space)

Description

elimCloseCoord reduces number of rows in 'dat' by eliminating lines where x & y coordinates (columns of matrix 'dat' defined by 'useCol') are identical (overlay points) or very close. The stringency for 'close' values may be fine-tuned using nDig), this function uses internally firstOfRepeated.

Usage

elimCloseCoord(
  dat,
  useCol = 1:2,
  elimIdentOnly = FALSE,
  refine = 2,
  nDig = 3,
  callFrom = NULL,
  silent = FALSE
)

Arguments

Value

resultant matrix/data.frame

See Also

findCloseMatch, firstOfRepeated

Examples

da1 <- matrix(c(rep(0:4,5),0.01,1.1,2.04,3.07,4.5),nc=2); da1[,1] <- da1[,1]*99; head(da1)
elimCloseCoord(da1)

Equal character-length number

Description

equLenNumber convert numeric entry 'x' to text, with all elements getting the same number of characters (ie by adding preceeding or tailing 0s, if needed). So far, the function cannot handle scientific annotations.

Usage

equLenNumber(x, silent = FALSE, callFrom = NULL, debug = FALSE)

Arguments

Value

character vector formated as equal number of characters per value

See Also

sprintf

Examples

equLenNumber(c(12,-3,321))
equLenNumber(c(12,-3.3,321))

Exclude extreme values (based on distance to mean)

Description

This function aims to identify extreme values (values most distant to mean, thus potential outlyers), mark them as NA or directely exclude them (depending on 'showNAs'). Note that every set of non-identical values will have at least one most extreme value. Extreme values are part of many distributions, they are not necessarily true outliers.

Usage

exclExtrValues(
  dat,
  result = "val",
  CVlim = NULL,
  maxExcl = 1,
  showNA = FALSE,
  goodValues = TRUE,
  silent = FALSE,
  callFrom = NULL
)

Arguments

Value

numeric vector wo extremle values or index-position of extreme values

See Also

firstOfRepLines, get1stOfRepeatedByCol for treatment of matrix

Examples

x <- c(rnorm(30),-6,20)
exclExtrValues(x)

Normalize by adjusting exponent

Description

This function normalizes 'dat' by optimizing exponent function (ie dat ^exp) to fit best to 'ref' (default: average of each line of 'dat').

Usage

exponNormalize(
  dat,
  useExpon,
  dynExp = TRUE,
  nStep = 20,
  startExp = 1,
  simMeas = "cor",
  refDat = NULL,
  refGrp = NULL,
  refLines = NULL,
  rSquare = FALSE,
  silent = FALSE,
  callFrom = NULL
)

Arguments

Value

This functuion returns a matrix of normalized data

See Also

more eveolved than normalizeThis with arugment set to 'exponent'

Examples

set.seed(2016); dat1 <- matrix(c(runif(200)+rep(1:10,20)),nc=10)
head(rowGrpCV(dat1,gr=gl(4,3,labels=LETTERS[1:4])[2:11]))
set.seed(2016); dat1 <- c(0.1,0.2,0.3,0.5)*rep(c(1,10),each=4)
dat1 <- matrix(round(c(sqrt(dat1),dat1^1.5,3*dat1+runif(length(dat1))),2),nc=3)
dat2a <- exponNormalize(dat1[,1],useExpon=2,nSte=1,refD=dat1[,3])
layout(matrix(1:2,nc=2))
plot(dat1[,1],dat1[,3],type="b",main="init",ylab="ref") 
plot(dat2a$datNor[,1],dat1[,3],type="b",main="norm",ylab="ref")
dat2b <- exponNormalize(dat1[,1],useExpon=c(1.7,2.3),nSte=5,refD=dat1[,3])
plot(dat1[,1],dat1[,3],type="b",main="init",ylab="ref")
plot(dat2b$datNor[,1],dat1[,3],type="b",main="norm",ylab="ref")

dat2c <- exponNormalize(dat1[,-3],useExpon=matrix(c(1.7,2.3,0.6,0.8),nc=2),nSte=5,refD=dat1[,3]);
plot(dat1[,1],dat1[,3],type="b",main="init",ylab="ref ")
plot(dat2c$datNor[,1],dat1[,3],type="b",main="norm 1",ylab="ref")
plot(dat1[,2],dat1[,3],type="b",main="init",ylab="ref")
plot(dat2c$datNor[,2],dat1[,3],type="b",main="norm 2",ylab="ref"); 

Extract just one series, ie channel, of list of arrays

Description

This function was designed for handeling measurements stored as list of multiple arrays, like eg compound-screens using microtiter-plates where multiple parameters ('channels') were recorded for each well (element). The elements (eg compounds screened) are typcally stored in the 1st dimension of the arrays, the replicated in the secon dimension and different measure types/parameters in the 3rd chanel. In order to keep the structure of of individual microtiter-plates, typically each plate forms a separate array (of same dimensions) in a list. The this function allows extracting a single channel of the list of arrays (3rd dim of each array) and return row-appended matrix.

Usage

extr1chan(arrLst, cha, na.rm = TRUE, rowSep = "__")

Arguments

Value

list with just single channel extracted

See Also

organizeAsListOfRepl

Examples

arr1 <- array(1:24,dim=c(4,3,2),dimnames=list(c(LETTERS[1:4]),
  paste("col",1:3,sep=""),c("ch1","ch2")))
arr2 <- array(74:51,dim=c(4,3,2),dimnames=list(c(LETTERS[1:4]),
  paste("col",1:3,sep=""),c("ch1","ch2")))
arrL1 <- list(pl1=arr1,pl2=arr2)
extr1chan(arrL1,ch=2)

Flexible extraction of columns

Description

This function provides flexible checking if a set of columns may be extracted from a matrix or data.frame 'x'. If argument extrCol is list of character vectors, this allows to search among given options, the first matching name for each vector will be identified.

Usage

extrColsDeX(x, extrCol, doExtractCols = FALSE, callFrom = NULL, silent = FALSE)

Arguments

Value

integer-vector (ifdoExtractCols=FALSE return depending on input matrix or data.frame)

See Also

read.table, filterList

Examples

dFr <- data.frame(a=11:14, b=24:21, cc=LETTERS[1:4], dd=rep(c(TRUE,FALSE),2)) 
extrColsDeX(dFr,c("b","cc","notThere")) 
extrColsDeX(dFr,c("b","cc","notThere"), doExtractCols=TRUE) 
extrColsDeX(dFr, list(c("nn","b","a"), c("cc","a"),"notThere")) 

Extract numeric part of matrix or data.frame

Description

extrNumericFromMatr extracts numeric part of matrix or data.frame, removing remaining non-numeric elements if trimToData is set to TRUE. Note, that cropping entire lines where a (single) text element appeared may quickly reduce the overal content of the input data.

Usage

extrNumericFromMatr(dat, trimToData = TRUE, silent = FALSE, callFrom = NULL)

Arguments

Value

matrix of numeric data

Examples

mat <- matrix(c(letters[1:7],14:16,LETTERS[1:6]),nrow=4,dimnames=list(1:4,letters[1:4]))
mat; extrNumericFromMatr(mat)
mat <- matrix(c(letters[1:4],1,"e",12:19,LETTERS[1:6]),nr=5,dimnames=list(11:15,letters[1:4]))
mat; extrNumericFromMatr(mat)

Extract specific text

Description

This function extracts/cuts text-fragments out of txt following specific anchors defined by arguments cutFrom and cutTo.

Usage

extrSpcText(
  txt,
  cutFrom = " GN=",
  cutTo = " PE=",
  missingAs = NA,
  exclFromTag = TRUE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Details

In case cutFrom is not found missingAs will be returned. In case cutTo is not found, text gets extracted with chaMaxEl characters.

Value

This function returns a modified character vector

See Also

substr

Examples

extrSpcText(c(" ghjg GN=thisText PE=001"," GN=_ PE=", NA, "abcd"))
extrSpcText(c("ABCDEF.3-6","05g","bc.4-5"), cutFr="\\.", cutT="-")

Extract last two numeric parts from character vector

Description

extractLast2numericParts extracts last 2 (integer) numeric parts between punctuations out of character vector 'x'. Runs faster than gregexpr . Note: won't work correctly with decimals or exponential signs !! (such characters will be considered as punctuation, ie as separator)

Usage

extractLast2numericParts(x, silent = FALSE, callFrom = NULL)

Arguments

Value

(numeric) matrix with 2 columns (eg from initial concatenated coordinates)

See Also

gregexpr from grep

Examples

extractLast2numericParts(c("M01.1-4","M001/2.5","M_0001_03-16","zyx","012","a1.b2.3-7,2"))

Filter three-dimensional array of numeric data

Description

Filtering of matrix or (3-dim) array x : filter column according to filtCrit (eg 'inf') and threshold filtVal

Usage

filt3dimArr(
  x,
  filtVal,
  filtTy = ">",
  filtCrit = NULL,
  displCrit = NULL,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Details

and extract/display all col matching 'displCrit'.

Value

This function returns a list of filtered matrixes (by 3rd dim)

See Also

filterList; filterLiColDeList;

Examples

arr1 <- array(11:34, dim=c(4,3,2), dimnames=list(c(LETTERS[1:4]),
  paste("col",1:3,sep=""), c("ch1","ch2")))
filt3dimArr(arr1,displCrit=c("col1","col2"),filtCrit="col2",filtVal=7)

Filter for unique elements

Description

This function aims to identify and remove duplicated elements in a list and maintain the list-structure in the output. filtSizeUniq filters 'lst' (list of character-vectors or character-vector) for elements being unique (to 'ref' or if NULL to all 'lst') and of character length. In addition, the min- and max- character length may be filtered, too. Eg, in proteomics this helps removing peptide sequences which would not be measured/detected any way.

Usage

filtSizeUniq(
  lst,
  ref = NULL,
  minSize = 6,
  maxSize = 36,
  filtUnique = TRUE,
  byProt = TRUE,
  inclEmpty = TRUE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

list of filtered input

See Also

correctToUnique, unique, duplicated

Examples

filtSizeUniq(list(A="a",B=c("b","bb","c"),D=c("dd","d","ddd","c")),filtUn=TRUE,minSi=NULL)
# input: c and dd are repeated
filtSizeUniq(list(A="a",B=c("b","bb","c"),D=c("dd","d","ddd","c")),ref=c(letters[c(1:26,1:3)],
  "dd","dd","bb","ddd"),filtUn=TRUE,minSi=NULL)  # a,b,c,dd repeated 

Filter lines(rows) and/or columns from all suitable elements of list

Description

Filter all elements of list (or S3-object) according to criteria designed to one selected reference-element of the list. All simple vectors, matrix, data.frames and 3-dimensional arrays will be checked if matching number of rows and/or columns to decide if they should be filtered the same way. If the reference element has same number of rows and columns simple (1-dimensional) vectors won't be filtered since it not clear if this should be done to lines or columns.

Usage

filterLiColDeList(
  lst,
  useLines,
  useCols = NULL,
  ref = 1,
  silent = FALSE,
  callFrom = NULL,
  debug = FALSE
)

Arguments

Details

This function is used eg in package wrProteo to simultaneaously filter raw and transformed data.

Value

This function returns the correct(ed) input (object of same class, of same length)

See Also

moderTest2grp for single comparisons, lmFit

Examples

lst1 <- list(m1=matrix(11:18,ncol=2), m2=matrix(21:30,ncol=2), indR=31:34,
  m3=matrix(c(21:23,NA,25:27,NA),ncol=2))
## here $m2 has more lines than $m1, and thus will be ignored when ref=1
filterLiColDeList(lst1, useLines=2:3)
filterLiColDeList(lst1, useLines="allNA", ref=4)

Filter for unique elements

Description

This function aims to apply a given filter-citerium, a matrix or vector of FALSE/TRUE which is typically combined with a second layer which filters for a min content of filer-passing values per line for the first/main criterium. Then all lines concerned will be removed. This will be done for all list-elements (of appropriate size) of the input-list (while maintaining the list-structure in the output) not matching the filtering criteria.

Usage

filterList(lst, filt, minLineRatio = 0.5, silent = FALSE, callFrom = NULL)

Arguments

Value

filtered list

See Also

correctToUnique, unique, duplicated, extrColsDeX

Examples

set.seed(2020); dat1 <- round(runif(80),2)
list1 <- list(m1=matrix(dat1[1:40],ncol=8), m2=matrix(dat1[41:80],ncol=8), other=letters[1:8])
rownames(list1$m1) <- rownames(list1$m2) <- paste0("line",1:5)
filterList(list1, list1$m1[,1] >0.4) 
filterList(list1, list1$m1 >0.4) 

Filter nodes & edges for extracting networks This function allows extracting and filtering network-data based on fixed threshold (limInt) and add sandwich-nodes (nodes inter-connecting initial nodes) out of node-based queries.

Description

Filter nodes & edges for extracting networks

This function allows extracting and filtering network-data based on fixed threshold (limInt) and add sandwich-nodes (nodes inter-connecting initial nodes) out of node-based queries.

Usage

filterNetw(
  lst,
  filtCol = 3,
  limInt = 5000,
  sandwLim = 5000,
  filterAsInf = TRUE,
  outFormat = "matrix",
  remOrphans = TRUE,
  remRevPairs = TRUE,
  elemNa = "genes",
  silent = FALSE,
  callFrom = NULL,
  debug = FALSE
)

Arguments

Value

This function returns a matrix or data.frame

See Also

in cbind

Examples

lst2 <- list('121'=data.frame(ID=as.character(c(141,221,228,229,449)),11:15), 
	 '131'=data.frame(ID=as.character(c(228,331,332,333,339)),11:15), 
  '141'=data.frame(ID=as.character(c(121,151,229,339,441,442,449)),c(11:17)), 
  '151'=data.frame(ID=as.character(c(449,141,551,552)),11:14),
  '161'=data.frame(ID=as.character(171),11), '171'=data.frame(ID=as.character(161),11),
  '181'=data.frame(ID=as.character(881:882),11:12) )

lst2 <- list('121'=data.frame(ID=as.character(c(141,221,228,229,449)),11:15, 21:25), 
	 '131'=data.frame(ID=as.character(c(228,331,332,333,339)),11:15, 21:25), 
  '141'=data.frame(ID=as.character(c(121,151,229,339,441,442,449)), c(11:17), 21:27), 
  '151'=data.frame(ID=as.character(c(449,141,551,552)), 11:14, 21:24),
  '161'=data.frame(ID=as.character(171), 11,21), '171'=data.frame(ID=as.character(161), 11,21),
  '181'=data.frame(ID=as.character(881:882), 11:12,21:22) )

(te1 <- filterNetw(lst2, limInt=90, remOrphans=FALSE))
(te2 <- filterNetw(lst2, limInt=90, remOrphans=TRUE))

(te3 <- filterNetw(lst2, limInt=13, remOrphans=FALSE))
(te4 <- filterNetw(lst2, limInt=13, remOrphans=TRUE))

Find Close Numeric Values Between Two Vectors

Description

findCloseMatch finds close matches (similar values) between two numeric vectors ('x','y') based on method 'compTy' and threshold 'limit'. Return list with close matches of 'x' to given 'y', the numeric value dependes on 'sortMatch' (if FALSE then always value of 'y' otherwise of longest of x&y). Note: Speed & memory improvement if 'sortMatch'=TRUE (but result might be inversed!): adopt search of x->y or y->x to searching matches of each longest to each shorter (ie flip x &y). Otherwise, if length of 'x' & 'y' are very different, it may be advantagous to use a long(er) 'x' and short(er) 'y' (with 'sortMatch'=FALSE). Note: Names of 'x' & 'y' or (if no names) prefix letters 'x' & 'y' are always added as names to results.

Usage

findCloseMatch(
  x,
  y,
  compTy = "ppm",
  limit = 5,
  asIndex = FALSE,
  maxFitShort = 100,
  sortMatch = FALSE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a list with close matches of 'x' to given 'y', the numeric value dependes on 'sortMatch' (if FASLE then always value of 'y' otherwise of longest of x&y)

See Also

checkSimValueInSer and (from this package) .compareByDiff, for convient output countCloseToLimits

Examples

aa <- 11:14 ; bb <- c(13.1,11.5,14.3,20:21)
findCloseMatch(aa,bb,com="diff",lim=0.6)
findCloseMatch(c(a=5,b=11,c=12,d=18),c(G=2,H=11,I=12,J=13)+0.5, comp="diff", lim=2)
findCloseMatch(c(4,5,11,12,18),c(2,11,12,13,33)+0.5, comp="diff", lim=2)
findCloseMatch(c(4,5,11,12,18),c(2,11,12,13,33)+0.5, comp="diff", lim=2, sort=FALSE)
.compareByDiff(list(c(a=10,b=11,c=12,d=13),c(H=11,I=12,J=13,K=33)+0.5),limit=1) #' return matrix

a2 <- c(11:20); names(a2) <- letters[11:20]
b2 <- c(25:5)+c(rep(0,5),(1:10)/50000,rep(0,6)); names(b2) <- LETTERS[25:5]
which(abs(b2-a2[8]) < a2[8]*1e-6*5)                                     #'  find R=18 : no10
findCloseMatch(a2, b2, com="ppm", lim=5)                                #'  find Q,R,S,T
findCloseMatch(a2, b2, com="ppm", lim=5,asI=TRUE)                       #'  find Q,R,S,T
findCloseMatch(b2, a2, com="ppm", lim=5,asI=TRUE,sort=FALSE)
findCloseMatch(a2, b2, com="ratio", lim=1.000005)                       #'  find Q,R,S,T
findCloseMatch(a2, b2, com="diff", lim=0.00005)                         #'  find S,T

Find repeated elements

Description

findRepeated gets index of repeated items/values in vector 'x' (will be treated as character). Return (named) list of indexes for each of the repeated values, or NULL if all values are unique. This approach is similar but more basic compared to get1stOfRepeatedByCol.

Usage

findRepeated(x, nonRepeated = FALSE, silent = FALSE, callFrom = NULL)

Arguments

Value

(named) list of indexes for each of the repeated values, or NULL if all values unique

See Also

similar approach but more basic than get1stOfRepeatedByCol

Examples

aa <- c(11:16,14:12,14); findRepeated(aa) 

Find similar numeric values from two vectors/matrixes

Description

findSimilFrom2sets compares to vectors or matrixes and returns combined view including only all close (by findCloseMatch). Return matrix (predMatr) with add'l columns for index to and 'grp' (group of similar values (1-to-many)), 'nGrp' (n of grp), 'isBest' or 'nBest', 'disToMeas' (distance/difference between pair) & 'ppmToPred' (distance in ppm). Note: too wide 'limitComp' will result in large window and many 'good' hits will compete (and be mutually exlcuded) if selection 'bestOnly' is selected

Usage

findSimilFrom2sets(
  predMatr,
  measMatr,
  colMeas = 1,
  colPre = 1,
  compareTy = "diff",
  limitComp = 0.5,
  bestOnly = FALSE,
  silent = FALSE,
  callFrom = NULL,
  debug = FALSE
)

Arguments

Value

This function returns a matrix (predMatr) with add'l columns for index to and 'grp' (group of similar values (1-to-many)), 'nGrp' (n of grp), 'isBest' or 'nBest', 'disToMeas' (distance/difference between pair) & 'ppmToPred' (distance in ppm)

See Also

checkSimValueInSer findCloseMatch closeMatchMatrix

Examples

aA <- c(11:17); bB <- c(12.001,13.999); cC <- c(16.2,8,9,12.5,12.6,15.9,14.1)
aZ <-  matrix(c(aA,aA+20),ncol=2,dimnames=list(letters[1:length(aA)],c("aaA","aZ")))
cZ <-  matrix(c(cC,cC+20),ncol=2,dimnames=list(letters[1:length(cC)],c("ccC","cZ")))
findCloseMatch(cC,aA,com="diff",lim=0.5,sor=FALSE)
findSimilFrom2sets(aA,cC)
findSimilFrom2sets(cC,aA)
findSimilFrom2sets(aA,cC,best=FALSE)
findSimilFrom2sets(aA,cC,comp="ppm",lim=5e4,deb=TRUE)
findSimilFrom2sets(aA,cC,comp="ppm",lim=9e4,bestO=FALSE)
# below: find fewer 'best matches' since search window larger (ie more good hits compete !)
findSimilFrom2sets(aA,cC,comp="ppm",lim=9e4,bestO=TRUE)      

Select groups within given range

Description

This function aims to help finding streches/segments of data with a given maximum number of NA-instances. This function is used to inspect/filter each lines of 'dat' for a subset with sufficient presence/absence of NA values (ie limit number of NAs per level of 'grp'). Note : optimal perfomance with n.lines >> n.groups

Usage

findUsableGroupRange(dat, grp, maxNA = 1, callFrom = NULL)

Arguments

Value

matrix with boundaries of 1st and last usable column (NA if there were no suitable groups found)

Examples

dat1 <- matrix(1:56,nc=7)
dat1[c(2,3,4,5,6,10,12,18,19,20,22,23,26,27,28,30,31,34,38,39,50,54)] <- NA
rownames(dat1) <- letters[1:nrow(dat1)]
findUsableGroupRange(dat1,gl(3,3)[-(3:4)])

Filter matrix to keep only first of repeated lines

Description

This function aims to reduce the complexity of a matrix (or data.frame) in case column 'refCol' has multiple lines with same value. In this case, it reduces the input-data to 1st line of redundant entries and returns a matrix (or data.frame) without lines identified as redundant entries for 'refCol'). in sum, this functions works lile useng unique on a given column, and propagates the same treatment to all other columns.

Usage

firstLineOfDat(dat, refCol = 2, silent = FALSE, debug = FALSE, callFrom = NULL)

Arguments

Value

matrix (same number of columns as input)

See Also

firstOfRepeated, unique, duplicated

Examples

(mat1 <- matrix(c(1:6,rep(1:3,1:3)),ncol=2,dimnames=list(letters[1:6],LETTERS[1:2])))
firstLineOfDat(mat1)

Reduce to first occurance of repeated lines

Description

This function concatenattes all columns of input-matrix and then searches like unique for unique elements, optionally the indexes of unique elements may get returned. Note: This function reats input as character (thus won't understand 10==10.0 ). Returns simplified/non-redundant vector/matrix (ie fewer lines), or respective index. faster than firstOfRepeated

Usage

firstOfRepLines(
  mat,
  outTy = "ind",
  useCol = NULL,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

simplified/non-redundant vector/matrix (ie fewer lines for matrix), or respective index

See Also

unique, nonAmbiguousNum, faster than firstOfRepeated which gives more detail in output (lines/elements/indexes of omitted)

Examples

mat <- matrix(c("e","n","a","n","z","z","n","z","z","b", 
  "","n","c","n","","","n","","","z"),ncol=2)
firstOfRepLines(mat,out="conc")

Find first of repeated elements

Description

This function works similar to unique, but provides additional information about which elements of original input 'x' are repeatd by providing indexes realtoe to the input. firstOfRepeated makes list with 3 elements : $indRepeated.. index for first of repeated 'x', $indUniq.. index of all unique + first of repeated, $indRedund.. index of all redundant entries, ie non-unique (wo 1st). Used for reducing data to non-redundant status, however, for large numeric input the function nonAmbiguousNum() may perform better/faster. NAs won't be considered (NAs do not appear in reported index of results), see also firstOfRepLines() .

Usage

firstOfRepeated(x, silent = FALSE, debug = FALSE, callFrom = NULL)

Arguments

Value

list with indices: $indRepeated, $indUniq, $indRedund

See Also

duplicated, nonAmbiguousNum, firstOfRepLines gives less detail in output (lines/elements/indexes of omitted not directly accessible) and works fsster

Examples

x <- c(letters[c(3,2:4,8,NA,3:1,NA,5:4)]); names(x) <- 100+(1:length(x))
firstOfRepeated(x)
x[firstOfRepeated(x)$indUniq]          # only unique with names

Fuse annotation matrix to initial matrix

Description

In a number of instances experimental measurements and additional information (annotation) are provided by separate objects (matrixes) as they may not be generated the same time. The aim of this function is provide help when matching approprate lines for 2 sets of data (experimental measures in iniTab and annotation from annotTab) for fusing. fuseAnnotMatr adds suppelmental columns/annotation to an initial matrix iniTab : using column 'refIniT' as key (in iniTab) to compare with key 'refAnnotT' (from 'annotTab'). The columns to be added from annotTab must be chosen explicitely. Note: if non-unique IDs in iniTab : runs slow (but save) due to use of loop for each unique ID.

Usage

fuseAnnotMatr(
  iniTab,
  annotTab,
  refIniT = "Uniprot",
  refAnnotT = "combName",
  addCol = c("ensembl_gene_id", "description", "geneName", "combName"),
  debug = TRUE,
  silent = FALSE,
  callFrom = NULL
)

Arguments

Value

combined matrix (elements not found in 'annotTab' are displayed as NA)

See Also

merge

Examples

tab0 <- matrix(rep(letters[1:25],8),ncol=10)
tab1 <- cbind(Uniprot=paste(tab0[,1],tab0[,2]),col1=paste(tab0[,3],
  tab0[,4],tab0[,5]," ",tab0[,7],tab0[,6]))
tab2 <- cbind(combName=paste(tab0[,1],tab0[,2]),col2=paste(tab0[,8],tab0[,9],tab0[,10]))
fuseAnnotMatr(tab1,tab2[c(20:11,2:5),],refIni="Uniprot",refAnnotT="combName",addCol="col2")
fuseAnnotMatr(tab2[c(20:11,2:5),],tab1,refAnnotT="Uniprot",refIni="combName",addCol="col1")

Fuse content of list-elements with redundant (duplicated) names

Description

This function fuses (character or numeric) elements of list re-occuring under same name, so that resultant list has unique names. Note : Will not work with list of matrixes

Usage

fuseCommonListElem(
  lst,
  initOrd = TRUE,
  removeDuplicates = FALSE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a fused list (same names as elements of input)

See Also

unlist

Examples

val1 <- 10 +1:26
names(val1) <- letters
lst1 <- list(c=val1[3:6],a=val1[1:3],b=val1[2:3],a=val1[12],c=val1[13])
fuseCommonListElem(lst1)

Fuse pairs to generate cluster-names

Description

Fuse previously identified pairs to 'clusters', return vector with cluster-numbers.

Usage

fusePairs(
  datPair,
  refDatNames = NULL,
  inclRepLst = FALSE,
  maxFuse = NULL,
  debug = FALSE,
  silent = TRUE,
  callFrom = NULL
)

Arguments